CN111128311B - Catalytic material screening method and system based on high-throughput experiment and calculation - Google Patents
Catalytic material screening method and system based on high-throughput experiment and calculation Download PDFInfo
- Publication number
- CN111128311B CN111128311B CN201911357752.2A CN201911357752A CN111128311B CN 111128311 B CN111128311 B CN 111128311B CN 201911357752 A CN201911357752 A CN 201911357752A CN 111128311 B CN111128311 B CN 111128311B
- Authority
- CN
- China
- Prior art keywords
- model
- catalytic
- catalyst
- catalytic material
- flux
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/10—Analysis or design of chemical reactions, syntheses or processes
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C10/00—Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/70—Machine learning, data mining or chemometrics
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C60/00—Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
Landscapes
- Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Theoretical Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Chemical & Material Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Crystallography & Structural Chemistry (AREA)
- Physics & Mathematics (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Analytical Chemistry (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Catalysts (AREA)
Abstract
The scheme provides a catalytic material screening method and a catalytic material screening system combining high-throughput experiments with high-throughput computation, wherein the method comprises the following steps: screening a catalytic material to be confirmed which meets the target catalytic performance by using a catalyst structure-activity relationship model constructed based on data results of high-flux experiments and high-flux calculation; performing high-flux preparation and high-flux performance evaluation on the screening result to obtain an experimental result of the catalytic material to be confirmed; and comparing the catalytic performance prediction result of the catalytic material to be confirmed with the experimental result of the catalytic material to be confirmed, and determining that the catalytic material to be confirmed is the catalytic material reaching the target catalytic performance under the condition of a preset deviation range. According to the scheme, a mutual-verification relation is formed among a theoretical calculation simulation result, an existing experiment result and a new catalyst characterization result through a catalyst structure-activity relation model constructed based on high-flux experiments and high-flux calculation, so that the screening precision and the screening speed of the catalytic materials are improved.
Description
Technical Field
The application relates to the field of catalytic material research and development, in particular to a catalytic material screening method and a catalytic material screening system combining high-throughput experiments and high-throughput calculation.
Background
The chemical reaction process related to the chemical production process and the energy storage and conversion process can be smoothly carried out by the corresponding catalyst. Catalysts are one of the most critical core technologies in many industries, and the performance of a catalyst determines whether a production process can be achieved, and its economics. Most of the current catalyst development efforts require extensive and time-consuming repeated tests to screen out the relatively ideal catalyst by experimental means only. With the ever-improving and continuous improvement of the theoretical method and the computing capability, people can establish the micro dynamics of the catalytic reaction by means of various different theoretical computing methods, further research the catalytic action mechanism of the catalyst, and finally, jointly reveal the reaction mechanism and the internal influence mechanism of the catalyst through the combination of theoretical computation and experiments.
Disclosure of Invention
The application provides a catalytic material screening method and system combining high throughput experimentation with high throughput computing.
According to a first aspect of embodiments of the present application, there is provided a catalytic material screening method based on high throughput experimentation and high throughput computing, the method comprising the steps of:
screening a catalytic material to be confirmed which meets the target catalytic performance by using a catalyst structure-activity relationship model constructed according to data results of high-flux experiments and high-flux calculation;
performing high-flux preparation and high-flux performance evaluation on the screening result to obtain an experimental result of the catalytic material to be confirmed;
and determining the catalytic material to be confirmed as the catalytic material reaching the target catalytic performance under the condition that the deviation of the catalytic performance prediction result of the catalytic material to be confirmed and the experimental result of the catalytic material to be confirmed is within a preset deviation range.
In a preferred embodiment, the construction step of the catalyst structure-activity relationship model constructed according to the data result of the high-throughput experiment and the high-throughput calculation includes:
training to obtain a structural feature-adsorption energy correlation model based on a machine learning algorithm by utilizing the structural features of reaction sites of the catalyst particle model and the adsorption energy of reactants, intermediate products and final products on different surface models;
training to obtain an adsorption energy-catalytic performance correlation model based on a machine learning algorithm by utilizing adsorption energy of reactants, intermediate products and final products on different surface models and theoretical catalytic performance of a catalyst particle model;
the theoretical catalytic performance of the catalyst particle model constructed according to the experimental characterization and the catalytic performance measured through experiments are utilized, and a correction model of a theoretical value and an experimental value of the catalytic performance is obtained through training based on a machine learning algorithm;
and obtaining a structure-adsorption-theoretical performance-actual performance correlation model, namely a catalyst structure-activity relation model, based on a machine learning algorithm by using the model.
In a preferred embodiment, the steps prior to constructing the catalyst structure-activity relationship model include:
preparing a catalyst at random in a high flux, evaluating the high flux performance of the catalyst, carrying out structural characterization on the catalyst after catalytic reaction, and constructing a catalyst surface model according to the characterization;
and constructing a catalyst particle model according to the surface energy of the surface model.
In a preferred embodiment, the previous step of constructing the catalyst structure-activity relationship model further comprises:
performing calculation simulation on the catalyst surface model;
and combining micro dynamics analysis to obtain the optimal reaction path and reaction rate of the main/side reaction.
Combining the reaction rate of the main/side reaction of the surface model and the proportion of each crystal face on the particle model, and converting the reaction rate and the proportion into catalytic reaction kinetic information of the particle model under the reaction environment condition;
and obtaining the theoretical catalytic performance of the catalyst model by using the reaction dynamics evaluation method.
In a preferred embodiment, the previous step of initially screening the catalytic material to be confirmed that meets the target catalytic performance by using a catalyst structure-activity relationship model constructed according to the data result of the high-throughput experiment and the high-throughput calculation comprises;
and (3) performing accuracy verification on a catalyst structure-activity relation model constructed based on the high-throughput experiment and the high-throughput calculation.
In a preferred embodiment, the step of verifying the accuracy of the catalyst structure-activity relationship model constructed based on the high-throughput experiment and the high-throughput calculation includes:
judging whether the prediction precision of the catalyst structure-activity relationship model meets the standard or not by utilizing training set data; the training set data includes: one or more of characterization data, a surface model phase diagram, a catalyst particle model, high-flux catalytic performance test data and theoretical catalytic performance of the catalyst model of the catalyst after the high-flux catalytic reaction;
if yes, the accuracy of the test set data is verified.
In a preferred embodiment, if not, the catalyst structure-activity relationship model is corrected by continuing training based on a machine learning algorithm.
In a preferred embodiment, the step of verifying the accuracy of the catalyst structure-activity relationship model further includes:
judging whether the prediction precision of the catalyst structure-activity relationship model meets the standard or not by using the test set data; the test set data includes: one or more of characterization data, a surface model phase diagram, a catalyst particle model, high-flux catalytic performance test data and theoretical catalytic performance of the catalyst model of the catalyst after the high-flux catalytic reaction;
if yes, the catalytic material to be confirmed meeting the target catalytic performance is initially screened according to a catalyst structure-activity relation model constructed based on a high-flux experiment and high-flux calculation.
In a preferred embodiment, if not, the accuracy verification of the training set data is re-performed.
In a preferred embodiment, if the deviation of the predicted catalytic performance result of the catalytic material to be confirmed predicted based on the catalyst structure-activity relationship model from the experimental result of the catalytic material to be confirmed is not within the predetermined deviation range; and combining a particle model, a catalytic performance theoretical value and an experimental value corresponding to the catalytic material to be confirmed, continuously training based on a machine learning algorithm, and correcting the catalyst structure-activity relation model.
According to a second aspect of embodiments of the present application, there is provided a catalytic material screening system based on high throughput experimentation and high throughput computing, the system comprising:
the screening unit is used for screening the catalytic material to be confirmed which meets the target catalytic performance according to the catalyst structure-activity relation model constructed based on the high-flux experiment and the high-flux calculation;
a confirmation unit for performing high-throughput preparation and high-throughput performance evaluation of the catalyst with respect to the screening result; and determining the catalytic material to be confirmed as the catalytic material reaching the target catalytic performance under the condition that the deviation of the catalytic performance prediction result of the catalytic material to be confirmed and the experimental result of the catalytic material to be confirmed is within a preset deviation range.
In a preferred embodiment, the system further comprises: a model construction unit; the model construction unit specifically performs the following steps:
training to obtain a structural feature-adsorption energy correlation model based on a machine learning algorithm by utilizing the structural features of reaction sites of the catalyst particle model and the adsorption energy of reactants, intermediate products and final products on different surface models;
training to obtain an adsorption energy-catalytic performance correlation model based on a machine learning algorithm by utilizing adsorption energy of reactants, intermediate products and final products on different surface models and theoretical catalytic performance of a catalyst particle model;
the theoretical catalytic performance of the catalyst particle model constructed according to the experimental characterization and the catalytic performance measured through experiments are utilized, and a correction model of a theoretical value and an experimental value of the catalytic performance is obtained through training based on a machine learning algorithm;
obtaining a structure-adsorption-theoretical performance-actual performance correlation model, namely a catalyst structure-activity relation model, based on a machine learning algorithm by utilizing the model; or alternatively, the process may be performed,
and according to the correction data, training is continued based on a machine learning algorithm, and the catalyst structure-activity relation model is corrected.
In a preferred embodiment, the system further comprises: and the accuracy verification unit is used for performing accuracy verification on the catalyst structure-activity relationship model based on the training set data and the test set data.
In a preferred embodiment, the system further comprises: providing a structural database and a catalytic performance database of basic data for the model building unit;
the structure database comprises: characterization data of the catalyst after random high-flux catalytic reaction, a surface model phase diagram and a catalyst particle model;
the catalytic performance database comprises: high throughput catalytic performance test data and theoretical catalytic performance of the catalyst model.
In a preferred embodiment, the confirmation unit further comprises: if the deviation between the predicted catalytic performance result of the catalytic material to be confirmed and the experimental result of the catalytic material to be confirmed is not within the preset deviation range based on the catalyst structure-activity relation model; feeding back a particle model, a catalytic performance theoretical value and a catalytic performance experimental value corresponding to the catalytic material to be confirmed to a model construction unit; the model building unit continues training based on a machine learning algorithm, and corrects the catalyst structure-activity relationship model.
Advantageous effects
According to the technical scheme, the theoretical calculation simulation result, the existing experimental result and the new catalyst characterization result can form a mutual-verification relation through the catalyst structure-activity relation model constructed based on the high-flux experiment and the high-flux calculation, so that the screening precision and the screening speed of the catalytic material are improved;
according to the technical scheme, the structure-activity relation model can be adjusted according to the update of experimental data, so that the difference problem between theoretical calculation simulation and experiments is solved through mutual evidence and mutual complementation of the experiment and theoretical calculation, and the aim of organically combining the theory and the experiment and preparing a high-performance target industrial catalyst under the guidance of the theory is fulfilled; the high flux rationality design catalyst is fully pushed to practical application, so that the design speed of the catalyst is greatly accelerated, and the development cost of the catalyst is greatly reduced;
the technical scheme is easy to realize, is simple to operate, and can automatically and intelligently finish the accurate screening of the catalytic materials.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 shows a schematic diagram of a catalytic material screening method based on high throughput experimentation and high throughput computing according to the present protocol;
fig. 2 shows a schematic diagram of a catalytic material screening method according to an embodiment of the present disclosure.
Detailed Description
In order to make the technical solutions and advantages of the embodiments of the present application more apparent, the following detailed description of exemplary embodiments of the present application is given with reference to the accompanying drawings, and it is apparent that the described embodiments are only some of the embodiments of the present application and not exhaustive of all the embodiments. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other.
Through analysis and research, although the combination of high-throughput theoretical calculation and high-throughput experimental means in the prior art has made a certain progress in the field of catalyst research and development, many problems still remain to be solved in the related research and development work. In most reported works, after the screening model is built by high-throughput computing, the screening model is not updated and revised due to the fact that the data is not updated in a rolling way, so that the built screening model has weak extrapolation capability, namely, the accuracy of searching for new materials is insufficient. In addition, since the model simulated by theoretical calculation is different from the actual material, after the prediction model is fitted by big data obtained by high-throughput calculation, a large deviation often occurs between the prediction result and the experimental result. Therefore, the scheme aims to provide a rapid screening method for catalytic materials with high flux and high accuracy, the database and the screening model are updated in real time, and in addition, the problem of difference between theoretical calculation simulation and experiment is solved through mutual verification and mutual complementation of the experiment and theoretical calculation, so that the theory and the experiment are organically combined.
As shown in fig. 1, the present solution provides a catalytic material screening method combining high throughput experimentation with high throughput computing, the method comprising the steps of:
s1, aiming at a certain type of catalyst, (formula and the like), preparing a catalytic material at random with high flux, and testing high-flux catalytic performance.
S2, characterizing the reacted catalyst, and guiding to build a catalyst surface model in a reaction environment.
The specific process for constructing the surface model according to the experimental characterization result comprises the following steps: the adsorption species, the distribution position and the proportion of the surface adsorption species of the catalyst after the reaction are identified by utilizing temperature programming desorption, the surface element composition of the catalyst is characterized by utilizing an X-ray photoelectron spectroscopy, the integral element distribution of the catalyst is characterized by utilizing an X-ray spectrometer in a line scanning way, and the integral element composition of the catalyst is characterized by utilizing ICP. And (3) characterizing the size of the catalyst particles and exposing crystal faces by using a high-resolution electron microscope.
S3, additionally building a surface model with common crystal face indexes, and drawing a phase diagram corresponding to the surface model.
In order to obtain a phase diagram of the surface model, the most stable coordination mode of reactant molecules or atoms and the surface model under specific reaction temperature and conditions needs to be determined by using the free energy of the surface Gibbs; determining the most stable distribution mode of all atoms in the surface model under specific reaction temperature and conditions by using the surface Gibbs free energy; the configuration of the surface model at the time of reaching adsorption equilibrium in the reaction environment was determined using Langmuir adsorption isotherms.
And S4, constructing a catalyst particle model according to the classical Wulff Construction rule and the surface energy of the surface model, and storing the catalyst particle model into a catalyst structure database.
S5, performing calculation simulation on the surface model by using first principle calculation software based on a reaction path network of the chemical reaction.
S6, combining with micro dynamics analysis to obtain an optimal reaction path and reaction rate of the main (side) reaction.
S7, combining the reaction rate of the main (side) reaction of the surface model and the proportion of each crystal face on the particle model, and converting the reaction rate and the proportion into catalytic reaction kinetic information of the particle model under the reaction environment condition.
S8, obtaining theoretical catalytic performance of the catalyst model in the structural database by using the reaction dynamics evaluation method, and storing the theoretical catalytic performance in the catalytic performance database.
The method for forming the theoretical evaluation reaction kinetics specifically comprises the following steps: the adsorption energy of the reactants, intermediates, end products on different surface models is calculated from the adsorption species contained in the reaction network. Searching the reaction path (namely elementary reaction) between each adsorption steady state, and acquiring corresponding information such as transition state structure, energy and the like and an activation energy barrier. The micromechanics analysis process is as follows: calculating a reaction rate constant of the primitive reaction through an Arrhenius equation, an activation energy barrier and a distribution function based on transition state vibration entropy; the coverage of each adsorbed species in the reaction network, and the reaction rate of each elementary reaction, are deduced from the steady-state approximation assumptions of the adsorption energy of the reactive species, the reaction rate constant of the elementary reactions, and the surface coverage of the reactive species. And finally, combining the reaction rate of the main (side) reaction of the surface model and the proportion of each crystal face on the particle model, and converting the reaction rate into the catalytic performance of the particle model under the specific reaction environment condition.
S9, dividing the data of the structure database and the catalytic performance database into two parts to serve as a training set and a testing set of a machine learning algorithm. The method comprises the steps of randomly dividing data of a structural database and a catalytic performance database into two parts, and using the two parts as a training set and a test set of a machine learning algorithm, wherein the data volume of the training set accounts for 50-70% of the total data volume, the data volume of the test set accounts for 20-30% of the total data volume, and dividing the training set by using a scikit-learn KFOLD method.
S10, extracting structural characteristics of reaction sites of a catalyst particle model in a structural database as input data, calculating and simulating adsorption energy of obtained reactants, intermediate products and final products as output data, and establishing a structural characteristic-adsorption energy correlation model. Wherein for an adsorption energy prediction model based on surface structure features. Because of the numerous physicochemical properties of the adsorption sites, redundant structural information needs to be removed, structural features with high correlation with the adsorption energy of reactant molecules are screened out, weight parameters are added, structural descriptors are constructed, and a linear or even nonlinear model between the structural descriptors and the adsorption energy is searched. The idea can be implemented by machine learning algorithms and building neural networks.
S11, taking the adsorption energy of the reactant, the intermediate product and the final product obtained by calculation simulation as input data, and taking the theoretical catalytic performance of the catalyst particle model as output data, and establishing an adsorption energy-catalytic performance correlation model.
S12, taking theoretical catalytic performance of a particle model constructed according to experimental characterization as input data, taking experimental catalytic performance as output data, and establishing a correction model of a theoretical value and an experimental value of the catalytic performance.
S13, taking a structure-adsorption-theoretical performance-actual performance correlation model, namely a catalyst structure-activity relation model, given by a machine learning model as an objective function, and aiming at a certain target catalytic performance, using the model to find an optimal solution of a target problem.
S14, screening out a catalytic material with excellent target catalytic performance according to the target function, and guiding the experiment to perform high-flux preparation and high-flux catalytic performance test.
S15, comparing the catalytic performance prediction result of the catalyst structure-activity relation model with the experimental result. If the deviation is within the acceptable range, the catalytic material is deemed to be successfully screened to achieve the target catalytic performance.
S16, if the predicted result of the catalytic performance has larger deviation from the experimental result, adding relevant data (including a particle model constructed according to experimental characterization and the catalytic performance obtained by experimental test) of the sample into a training set of the catalyst structure-activity relationship model, and further correcting and upgrading the structure-activity relationship model. And converting the updated structure-activity relation model into an objective function, and searching new experimental alternatives.
In order to cooperate with the implementation of the catalytic material screening method, the scheme further provides a catalytic material screening system, which comprises: a screening unit and a confirmation unit. The screening unit preliminarily screens the catalytic material to be confirmed which meets the target catalytic performance according to a catalyst structure-activity relation model constructed based on the data result of the high-flux experiment and the high-flux calculation; and then, utilizing the confirmation unit to predict the deviation of the catalytic performance prediction result of the catalytic material to be confirmed and the experimental result of the catalytic material to be confirmed based on the catalyst structure-activity relationship model, and determining that the catalytic material to be confirmed is the catalytic material reaching the target catalytic performance under the condition that the deviation is within a preset deviation range. In addition, the confirmation unit can continue to judge, if the deviation between the predicted catalytic performance result of the catalytic material to be confirmed and the experimental result of the catalytic material to be confirmed is not in the preset deviation range based on the catalyst structure-activity relation model; feeding back a particle model, a catalytic performance theoretical value and a catalytic performance experimental value corresponding to the catalytic material to be confirmed to a model construction unit; the model building unit continues training based on a machine learning algorithm, and corrects the catalyst structure-activity relationship model.
In the scheme, a model construction unit can be constructed in a screening system to construct a catalyst structure-activity relationship model based on a machine learning algorithm according to stored or dynamically updated experimental data; or, according to the correction data, correcting the catalyst structure-activity relation model based on a machine learning algorithm. The specific steps of the model construction unit for constructing the catalyst structure-activity relationship model include:
training to obtain a structural feature-adsorption energy correlation model based on a machine learning algorithm by utilizing the structural features of reaction sites of the catalyst particle model and the adsorption energy of reactants, intermediate products and final products on different surface models;
training to obtain an adsorption energy-catalytic performance correlation model based on a machine learning algorithm by utilizing adsorption energy of reactants, intermediate products and final products on different surface models and theoretical catalytic performance of a catalyst particle model;
the theoretical catalytic performance of the catalyst particle model constructed according to the experimental characterization and the catalytic performance measured through experiments are utilized, and a correction model of a theoretical value and an experimental value of the catalytic performance is obtained through training based on a machine learning algorithm;
and obtaining a structure-adsorption-theoretical performance-actual performance correlation model, namely a catalyst structure-activity relation model, based on a machine learning algorithm by using the model.
In the scheme, in order to improve the precision of the catalyst structure-activity relationship model, a precision verification unit is further arranged in the screening system; the accuracy verification unit can perform accuracy verification on the catalyst structure-activity relationship model based on the training set data and the test set data. The training set data and the test set data are obtained by randomly dividing the data of the structural database and the catalytic performance database into two parts, and are used as training sets and test sets of a machine learning algorithm, specifically, the training sets are divided by using a scikit-learn KFOLD method, wherein the data volume of the training sets accounts for 50-70% of the total data volume, and the data volume of the test sets accounts for 20-30% of the total data volume. The structure database comprises: characterization data of the catalyst after the high-flux catalytic reaction, a surface model phase diagram and a catalyst particle model; the catalytic performance database comprises: high throughput catalytic performance test data and theoretical catalytic performance of the catalyst model.
The present solution is further illustrated by way of example below.
As shown in fig. 2, the present example provides a catalytic material screening method combining high throughput experimentation with high throughput computing. Taking the flow of optimizing the direct synthesis of the hydrogen peroxide palladium-based alloy catalyst as an example, the steps of catalytic material screening are as follows:
s1, palladium and any transition metal element in any proportion are used as raw materials, and palladium-based alloy particles with any size distribution are prepared in a high flux manner and are loaded on alpha-Al 2 O 3 . And (5) performing high-flux direct synthesis hydrogen peroxide catalytic performance test.
S2, carrying out structural characterization on the reacted palladium-based alloy catalyst, and guiding to build a catalyst surface model in a reaction environment. The method comprises the following steps of: the distribution positions and the proportion of oxygen and hydrogen on the surface of the palladium-based alloy catalyst after the direct synthesis of hydrogen peroxide are identified by utilizing programmed temperature desorption, the surface element composition of the catalyst is characterized by utilizing an X-ray photoelectron spectroscopy, the integral element distribution of the catalyst is characterized by utilizing an X-ray spectrometer line sweep, and the integral element composition of the catalyst is characterized by utilizing ICP. And (3) characterizing the size of the catalyst particles and exposing crystal faces by using a high-resolution electron microscope.
S3, building a palladium-based alloy (the doping metal type considers all transition metals and the composition is in a large range of 0-1) surface model with common crystal face indexes. Since the surface energy and segregation energy are functions of chemical potentials (considering the ambient temperature and pressure) of oxygen molecules and hydrogen molecules, a phase diagram corresponding to the surface model can be drawn. In order to obtain the phase diagram of the palladium-based alloy surface model, the palladium-based alloy unit cell models with different compositions can be derived by using Material Studio software, and the surfaces with specific crystal orientation indexes can be cut. The surface Gibbs free energy is used to determine the most stable coordination mode of oxygen atoms, hydrogen atoms and palladium-based alloy surface model under specific reaction temperature and conditions. The most stable distribution mode of doping atoms and palladium atoms in a palladium-based alloy surface model under specific reaction temperature and conditions is determined by using the surface Gibbs free energy. And determining the configuration of the palladium-based alloy surface when the adsorption equilibrium is reached in the reaction environment of directly synthesizing hydrogen peroxide by using a Langmuir adsorption isotherm.
S4, constructing a palladium-based alloy catalyst particle model according to the classical Wulff Construction rule and the surface energy of the palladium-based alloy surface model, and storing the palladium-based alloy catalyst particle model into a catalyst structure database. In the step, according to the surface energy of different crystal planes obtained by quantitative calculation, a palladium-based alloy particle model with the lowest surface energy can be constructed by means of open source software WULFFMAN, and the proportion of each exposed crystal plane is given. And finally, acquiring the balance structure of the palladium-based alloy particles by adopting a molecular simulation program platform and a global optimization algorithm RPSO. Potential energy of interaction between metal atoms adopts common TB-SMA (TB-SMA) metal multimeric potential.
S5, performing Quantum chemical computation by using a software package Quantum Espresso based on a first sexual principle, performing computation simulation on a palladium-based alloy surface model based on a reaction path network of a direct hydrogen peroxide synthesis reaction, and obtaining adsorption energy of reactants, intermediate products and final products on the surface model, wherein the activation energy barrier of each elementary reaction
S6, combining micro dynamics analysis to obtain the reaction rate of each elementary reaction. Finding out the optimal reaction path of the main reaction for generating the target product hydrogen peroxide and the side reaction for generating the byproduct water, and calculating the reaction rate.
S7, combining the reaction rate of the main (side) reaction of the palladium-based alloy surface model and the proportion of each crystal face on the palladium-based alloy particles, and converting the main (side) reaction rate and the proportion of each crystal face into the palladium-based alloy catalyst particle model to catalyze the conversion rate of hydrogen and oxygen and the selectivity of hydrogen peroxide under the reaction environment condition.
S8, acquiring theoretical catalytic performance of a particle model formed by a palladium-based alloy surface model in the phase diagram by using the reaction dynamics evaluation method, and storing the theoretical catalytic performance into a catalytic performance database. The theoretical catalytic performance of the palladium-based alloy particle model constructed according to the experimental characterization is obtained by using the reaction dynamics evaluation method, and the theoretical catalytic performance and the experimental catalytic performance are stored in a catalytic performance database.
S9, dividing the data of the structure database and the catalytic performance database into two parts to serve as a training set and a testing set of a machine learning algorithm.
S10, extracting the electronic and geometric structural characteristics and the characteristic properties of contained elements of a reaction site of the palladium-based alloy particle model in a structural database, and using the candidate structural characteristics as input data, and calculating the adsorption energy of reactants, intermediate products and final products obtained by simulation on a surface model as output data. Establishing a first neural network: structural feature-adsorption energy correlation model. Specifically, an adsorption energy prediction model based on the surface structural characteristics of the palladium-based alloy is established. The neural network was trained using Adam algorithm and 10 fold cross validation based on deep feed forward extension network to model the structure-adsorption performance of individual atoms of the catalyst model. The hyper-parameters of the neural network are derived from a grid search. Adding the adsorption energy of all atoms in the system to obtain the integral adsorption performance. The model is written based on Python and TensorFlow. In this embodiment, the machine learning algorithm builds a structure descriptor based on the electronegativity ratio of the doping element to the palladium element.
S11, taking adsorption energy as input data and taking theoretical catalytic performance of the palladium-based alloy particle model as output data. Establishing a second neural network: adsorption energy-catalytic performance correlation model.
S12, taking theoretical catalytic performance of the palladium-based alloy particle model constructed according to experimental characterization as input data and taking experimental measured catalytic performance as output data. Establishing a third neural network: and (3) a correction model of the theoretical value and the experimental value of the catalytic performance.
S13, taking hydrogen peroxide selectivity as target catalytic performance, and using a structure-adsorption-theoretical performance-actual performance correlation model given by a machine learning model, namely directly synthesizing a structure-activity relation model of the hydrogen peroxide palladium-based catalyst, as a target function, so as to find an optimal formula for improving hydrogen peroxide selectivity. In this step, the structure-activity relationship model given by the machine learning algorithm is converted into an objective function. Randomly generating an initial palladium-based alloy particle model by using a global optimization algorithm RPSO, and randomly generating the initial speed of each atom in the palladium-based alloy particle model; performing force balance optimization calculation on the coordinates of atoms in the palladium-based alloy particle model by using a quasi-Newton algorithm to obtain a structure of a new palladium-based alloy particle model after local optimization; predicting the catalytic performance of the palladium-based alloy particle model after local optimization by using the structure-activity relation model; updating the speed and the coordinate of atoms in the palladium-based alloy particle model through the combination of a coordinate updating formula and a random learning operator in an RPSO algorithm, randomly generating a new palladium-based alloy particle model with the same size as the previous palladium-based alloy particle model again, and carrying out force balance optimization calculation on the speed and the coordinate of the atoms in the new palladium-based alloy particle model by using a quasi-Newton algorithm to obtain the structure of the new palladium-based alloy particle model after local optimization; and predicting the catalytic performance of the new palladium-based alloy particle model after local optimization by using the catalyst structure-activity relationship model, and taking the palladium-based alloy particle model with high catalytic performance in the new palladium-based alloy particle model after local optimization and the previous palladium-based alloy particle model as the preferential palladium-based alloy particle model.
S14, a palladium-based alloy catalyst particle model is screened according to an objective function, and the experiment is guided to carry out high-flux preparation and high-flux catalytic performance test.
S15, comparing the hydrogen peroxide selectivity prediction result of the structure-activity relation model with the experimental result. If the deviation is within 10%, the palladium-based alloy catalyst with the hydrogen peroxide selectivity reaching the standard is successfully screened out.
S16, if the deviation between the predicted result of hydrogen peroxide selectivity and the experimental result is greater than 10%, adding relevant data (comprising the palladium-based alloy catalyst particle model constructed according to experimental characterization and hydrogen peroxide selectivity obtained through experimental test) of the sample into a training set of a machine learning model, and further correcting and upgrading the structure-activity relation model. And converting the updated structure-activity relation model into an objective function, and searching new experimental alternatives.
In summary, the scheme can realize the real-time update of the database and the structure-activity relation model, and also builds a bridge for mutual verification between the theoretical calculation simulation result, the existing experimental result and the new catalyst characterization result, thereby improving the accuracy and the screening speed of the catalytic material screening. Meanwhile, a set of efficient and high-precision industrial catalyst auxiliary research and development platform is formed, repeated processes of theoretical screening, high-performance material preparation characterization, theoretical screening, … are realized, and the difference problem between theoretical calculation simulation and experiments is solved through mutual verification and mutual complementation of experiments and theoretical calculation, so that the theory and the experiments are organically combined, a high-performance target industrial catalyst is prepared under the guidance of the theory, a truly high-flux rational design catalyst is achieved, the design speed of the catalyst is greatly increased, and the development cost of the catalyst is greatly reduced. The scheme is easy to realize, simple to operate and capable of being realized automatically and intelligently.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof, but rather as providing for the use of additional embodiments and advantages of all such modifications, equivalents, improvements and similar to the present invention are intended to be included within the scope of the present invention as defined by the appended claims.
Claims (8)
1. The catalytic material screening method based on high-throughput experiments and calculation is characterized by comprising the following steps of:
screening a catalytic material to be confirmed which meets the target catalytic performance by using a catalyst structure-activity relationship model constructed based on data results of high-flux experiments and high-flux calculation;
performing high-flux preparation and high-flux performance evaluation on the screening result to obtain an experimental result of the catalytic material to be confirmed;
under the condition that the deviation between the predicted catalytic performance result of the catalytic material to be confirmed and the experimental result of the catalytic material to be confirmed is within a preset deviation range, determining that the catalytic material to be confirmed is the catalytic material reaching the target catalytic performance;
the construction step of the catalyst structure-activity relation model constructed based on the data result of the high-flux experiment and the high-flux calculation comprises the following steps:
training to obtain a structural feature-adsorption energy correlation model based on a machine learning algorithm by utilizing the structural features of reaction sites of the catalyst particle model and the adsorption energy of reactants, intermediate products and final products on different surface models;
training to obtain an adsorption energy-catalytic performance correlation model based on a machine learning algorithm by utilizing adsorption energy of reactants, intermediate products and final products on different surface models and theoretical catalytic performance of a catalyst particle model;
the theoretical catalytic performance of the catalyst particle model constructed according to the experimental characterization and the catalytic performance measured through experiments are utilized, and a correction model of a theoretical value and an experimental value of the catalytic performance is obtained through training based on a machine learning algorithm;
obtaining a structure-adsorption-theoretical performance-actual performance correlation model, namely a catalyst structure-activity relation model, based on a machine learning algorithm by utilizing the model;
the steps before constructing the catalyst structure-activity relation model comprise:
preparing a catalyst at random in a high flux, evaluating the high flux performance of the catalyst, carrying out structural characterization on the catalyst after catalytic reaction, and constructing a catalyst surface model according to the characterization;
constructing a catalyst particle model according to the surface energy of the surface model;
the previous step of constructing the catalyst structure-activity relationship model further comprises the following steps:
performing calculation simulation on the catalyst surface model;
obtaining an optimal reaction path and reaction rate of main/side reaction;
combining the reaction rate of the main/side reaction of the surface model and the proportion of each crystal face on the particle model, and converting the reaction rate and the proportion into catalytic reaction kinetic information of the particle model under the reaction environment condition;
and obtaining the theoretical catalytic performance of the catalyst model by utilizing the reaction kinetic information.
2. The method according to claim 1, wherein the previous step of screening the catalytic material to be confirmed that satisfies the target catalytic performance using the catalyst structure-activity relationship model constructed from the data result of the high-throughput experiment and the high-throughput calculation comprises;
performing accuracy verification on a catalyst structure-activity relation model constructed based on a high-throughput experiment and high-throughput calculation;
the step of verifying the accuracy of the catalyst structure-activity relation model constructed based on the high-flux experiment and the high-flux calculation comprises the following steps:
judging whether the prediction precision of the catalyst structure-activity relationship model meets the standard or not by utilizing training set data; the training set data includes: one or more of characterization data, a surface model phase diagram, a catalyst particle model, high-flux catalytic performance test data and theoretical catalytic performance of the catalyst model of the catalyst after the high-flux catalytic reaction;
if yes, performing accuracy verification of the test set data;
if not, continuing training based on a machine learning algorithm, and correcting the catalyst structure-activity relation model;
the step of verifying the accuracy of the catalyst structure-activity relation model based on the high-flux experiment and the high-flux calculation further comprises the following steps:
judging whether the prediction precision of the catalyst structure-activity relationship model meets the standard or not by using the test set data; the test set data includes: one or more of characterization data, a surface model phase diagram, a catalyst particle model, high-flux catalytic performance test data and theoretical catalytic performance of the catalyst model of the catalyst after the high-flux catalytic reaction;
if yes, preliminarily screening the catalytic material to be confirmed which meets the target catalytic performance according to a catalyst structure-activity relation model constructed based on a high-flux experiment and high-flux calculation;
if not, the accuracy verification of the training set data is carried out again.
3. The catalytic material screening method according to claim 1, wherein if a deviation of a predicted catalytic performance result of the catalytic material to be confirmed, which is predicted based on the catalyst structure-activity relationship model, from an experimental result of the catalytic material to be confirmed is not within a predetermined deviation range; and combining a particle model, a catalytic performance theoretical value and an experimental value corresponding to the catalytic material to be confirmed, continuously training based on a machine learning algorithm, and correcting the catalyst structure-activity relation model.
4. A catalytic material screening system for use in a high throughput experimentation and calculation based catalytic material screening method as claimed in claim 1, the system comprising:
the screening unit is used for screening the catalytic material to be confirmed which meets the target catalytic performance by utilizing a catalyst structure-activity relationship model constructed based on the data result of the high-flux experiment and the high-flux calculation;
a confirmation unit for performing high-throughput preparation and high-throughput performance evaluation of the catalyst with respect to the result of the above screening; and determining the catalytic material to be confirmed as the catalytic material reaching the target catalytic performance under the condition that the deviation of the catalytic performance prediction result of the catalytic material to be confirmed and the experimental result of the catalytic material to be confirmed is within a preset deviation range.
5. The catalytic material screening system of claim 4, further comprising: a model construction unit; the model construction unit specifically performs the following steps:
training to obtain a structural feature-adsorption energy correlation model based on a machine learning algorithm by utilizing the structural features of reaction sites of the catalyst particle model and the adsorption energy of reactants, intermediate products and final products on different surface models;
training to obtain an adsorption energy-catalytic performance correlation model based on a machine learning algorithm by utilizing adsorption energy of reactants, intermediate products and final products on different surface models and theoretical catalytic performance of a catalyst particle model;
the theoretical catalytic performance of the catalyst particle model constructed according to the experimental characterization and the catalytic performance measured through experiments are utilized, and a correction model of a theoretical value and an experimental value of the catalytic performance is obtained through training based on a machine learning algorithm;
obtaining a structure-adsorption-theoretical performance-actual performance correlation model, namely a catalyst structure-activity relation model, based on a machine learning algorithm by utilizing the model; or alternatively, the process may be performed,
and according to the correction data, training is continued based on a machine learning algorithm, and the catalyst structure-activity relation model is corrected.
6. The catalytic material screening system of claim 4, further comprising: and the accuracy verification unit is used for performing accuracy verification on the catalyst structure-activity relationship model based on the training set data and the test set data.
7. The catalytic material screening system of claim 5, further comprising: providing a structural database and a catalytic performance database of basic data for the model building unit;
the structure database comprises: characterization data of the catalyst after random high-flux catalytic reaction, a surface model phase diagram and a catalyst particle model;
the catalytic performance database comprises: high throughput catalytic performance test data and theoretical catalytic performance of the catalyst model.
8. The catalytic material screening system according to claim 5, wherein the validation unit further comprises: if the deviation between the predicted catalytic performance result of the catalytic material to be confirmed and the experimental result of the catalytic material to be confirmed is not within the preset deviation range based on the catalyst structure-activity relation model; the particle model, the catalytic performance theoretical value and the catalytic performance experimental value corresponding to the catalytic material to be confirmed are used as correction data to be fed back to the model construction unit; the model building unit continues training based on a machine learning algorithm, and corrects the catalyst structure-activity relationship model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911357752.2A CN111128311B (en) | 2019-12-25 | 2019-12-25 | Catalytic material screening method and system based on high-throughput experiment and calculation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911357752.2A CN111128311B (en) | 2019-12-25 | 2019-12-25 | Catalytic material screening method and system based on high-throughput experiment and calculation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111128311A CN111128311A (en) | 2020-05-08 |
| CN111128311B true CN111128311B (en) | 2023-04-28 |
Family
ID=70502232
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911357752.2A Active CN111128311B (en) | 2019-12-25 | 2019-12-25 | Catalytic material screening method and system based on high-throughput experiment and calculation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111128311B (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113012768B (en) * | 2021-03-03 | 2023-03-17 | 上海大学 | Construction method and application of high-molecular flame-retardant composite material gene library |
| CN113205861B (en) * | 2021-04-13 | 2023-04-07 | 浙江大学 | Method for predicting pore structure of SCR (selective catalytic reduction) catalyst based on machine learning technology |
| CN113192575B (en) * | 2021-04-25 | 2023-07-25 | 北京化工大学 | Alloy catalytic material screening method and device |
| CN113963756B (en) * | 2021-05-18 | 2022-10-11 | 杭州剂泰医药科技有限责任公司 | Platform and method for developing prescription of pharmaceutical preparation |
| WO2022260172A1 (en) * | 2021-06-11 | 2022-12-15 | Ｅｎｅｏｓ株式会社 | Search device, search method, program, and non-transitory computer-readable medium |
| CN114038512B (en) * | 2021-11-02 | 2024-04-30 | 卫星化学股份有限公司 | Catalytic condition optimization method and system for synthesizing acrylic acid |
| CN113908863A (en) * | 2021-11-16 | 2022-01-11 | 西南石油大学 | Fracturing flowback fluid purification experimental method based on photocatalytic reaction |
| WO2023097664A1 (en) * | 2021-12-03 | 2023-06-08 | 深圳先进技术研究院 | Robot-assisted intelligent inverse design for automatic nanocrystal characterization acceleration material |
| WO2023097644A1 (en) * | 2021-12-03 | 2023-06-08 | 深圳先进技术研究院 | Robot-assisted digital controllable nanocrystal synthesis method based on thermodynamic morphology control mechanism |
| CN114678087B (en) * | 2022-03-09 | 2022-11-25 | 南京邮电大学 | Automatic acquisition and processing method and system for high-throughput material calculation data |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2002077772A2 (en) * | 2001-03-23 | 2002-10-03 | Transform Pharmaceuticals, Inc. | Method and system for high-throughput screening |
| CN101477597A (en) * | 2009-01-15 | 2009-07-08 | 浙江大学 | Natural product active ingredient computation and recognition method based compound characteristic |
| CN109658988A (en) * | 2018-12-20 | 2019-04-19 | 中海石油炼化有限责任公司 | A kind of Hydrobon catalyst performance prediction method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080168014A1 (en) * | 2006-12-27 | 2008-07-10 | Bhagat Phiroz M | Catalyst discovery through pattern recognition-based modeling and data analysis |
-
2019
- 2019-12-25 CN CN201911357752.2A patent/CN111128311B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2002077772A2 (en) * | 2001-03-23 | 2002-10-03 | Transform Pharmaceuticals, Inc. | Method and system for high-throughput screening |
| CN101477597A (en) * | 2009-01-15 | 2009-07-08 | 浙江大学 | Natural product active ingredient computation and recognition method based compound characteristic |
| CN109658988A (en) * | 2018-12-20 | 2019-04-19 | 中海石油炼化有限责任公司 | A kind of Hydrobon catalyst performance prediction method |
Non-Patent Citations (2)
| Title |
|---|
| Zheng Li等.High-throughput screening of bimetallic catalysts enabled by machine learning.《Royal Society of Chemistry》.2017,全文. * |
| 程道建.基于结构描述符的催化剂涉及方法及应用.《中国化学会第十九届全国催化学术会议摘要集》.2019,全文. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111128311A (en) | 2020-05-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111128311B (en) | Catalytic material screening method and system based on high-throughput experiment and calculation | |
| CN111177915B (en) | High-flux calculation method and system for catalytic material | |
| Devinyak et al. | 3D-MoRSE descriptors explained | |
| Rackers et al. | Tinker 8: software tools for molecular design | |
| Vanduyfhuys et al. | QuickFF: A program for a quick and easy derivation of force fields for metal‐organic frameworks from ab initio input | |
| Bonthuis et al. | Optimization of classical nonpolarizable force fields for OH− and H3O+ | |
| Giese et al. | Density-functional expansion methods: Evaluation of LDA, GGA, and meta-GGA functionals and different integral approximations | |
| Margraf et al. | Exploring catalytic reaction networks with machine learning | |
| JP4774523B2 (en) | Quantum state estimation method, quantum state estimation device, and computer program | |
| Luppi et al. | Six-dimensional potential energy surface for H 2 at Ru (0001) | |
| He et al. | Recent progress in general force fields of small molecules | |
| Sánchez-Márquez et al. | On electronegativity, hardness, and reactivity descriptors: a new property-oriented basis set | |
| Zhong et al. | Vibrational mode frequency correction of liquid water in density functional theory molecular dynamics simulations with van der Waals correction | |
| WO2016052662A1 (en) | Free energy calculation device, method, program, and recording medium with said program recorded thereon | |
| Suzuki et al. | A transformation theory of stochastic evolution in Red Moon methodology to time evolution of chemical reaction process in the full atomistic system | |
| Guzel et al. | 4D-QSAR studies using a new descriptor of the Klopman index: antibacterial activities of Sulfone derivatives containing 1, 3, 4-Oxadiazole moiety based on MCET model | |
| Evans et al. | matador: a Python library for analysing, curating and performing high-throughput density-functional theory calculations | |
| De Fazio et al. | Benchmark quantum mechanical calculations of vibrationally resolved cross sections and rate constants on ab initio potential energy surfaces for the F+ HD reaction: comparisons with experiments | |
| Gertig et al. | CAT-COSMO-CAMPD: Integrated in silico design of catalysts and processes based on quantum chemistry | |
| JP2022102726A (en) | Learning device and prediction device of activation energy in chemical reaction | |
| Rivera-Rivera et al. | Morphing a vibrationally-complete ground state potential for the hydrogen bond OC–HF | |
| Chi et al. | Photoelectron Imaging of Ag–(H2O) x and AgOH–(H2O) y (x= 1, 2, y= 0–4) | |
| Caldeweyher et al. | An open-source framework for fast-yet-accurate calculation of quantum mechanical features | |
| Liu et al. | Molecular Dynamics Simulations and Product Vibrational Spectral Analysis for the Reactions of NO2 with 1-Ethyl-3-methylimidazolium Dicyanamide (EMIM+ DCA–), 1-Butyl-3-methylimidazolium Dicyanamide (BMIM+ DCA–), and 1-Allyl-3-methylimidazolium Dicyanamide (AMIM+ DCA–) | |
| Yu et al. | Theoretical Study of FH2–Electron Photodetachment Spectra on New Ab Initio Potential Energy Surfaces |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |