CN115796346B - Yield optimization method, system and non-transitory computer readable storage medium - Google Patents
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Abstract
The invention relates to a yield optimization method, a system and a non-transitory computer readable storage medium, wherein the method comprises the following steps: s1, selecting reaction parameters affecting the yield of chemical reaction, and respectively coding the numerical reaction parameters and the non-numerical reaction parameters; s2, carrying out Cartesian product combination operation on values corresponding to all reaction parameters to obtain all sets, namely a reaction parameter space; s3, selecting data in a reaction parameter space, performing a chemical reaction experiment to obtain yield data, constructing a model by using a Bayesian optimization algorithm, and recommending an experimental parameter combination of the next round; and S4, carrying out specific experimental verification according to the experimental parameter combination recommended in the step S3, and carrying out multiple-round interaction with the algorithm in the step S3 to finally complete the yield optimization of the whole chemical reaction, thereby obtaining a satisfactory yield. The method can effectively improve the experimental efficiency of optimizing the yield of the chemical reaction, and brings great convenience to the research and development work of chemical experiments.
Description
Technical Field
The invention relates to a yield optimization method, a system and a non-transitory computer readable storage medium, belonging to the technical field of yield optimization methods.
Background
Currently, optimization of chemical reaction yields is a very challenging task, requiring an expert in chemical field to evaluate various reaction parameters such as substrate, additives, solvents, concentrations, catalysts, temperature, etc. Due to time and experience accumulation limitations, an expert can only simply evaluate a small fraction of these conditions during a standard optimization process. It is also very challenging to achieve a higher yield. Although several thousands of experiments can be completed in a short time by means of high throughput experiments, the cost consumption is very great. In addition, by looking at the relevant literature, experience accumulation of similar reactions, and understanding of the chemical mechanism of the reactants can play an important role in yield optimization, but this also places very high professional demands on the relevant experimenters.
To solve these problems, chemical specialists often perform control experiments by using some experimental design methods, such as designing experimental schemes by using DOE experimental design ideas, but there are also some problems of optimizing designs and selecting experience priorities. Yield optimization is essentially a parameter optimization, so bayesian optimization algorithms in machine learning can be employed. The algorithm aims at balancing exploration of the uncertainty field and utilization of available information, thereby achieving a high quality configuration in fewer evaluations. Meanwhile, the algorithm supports parallel calculation of a plurality of experiments, which means that a plurality of groups of experiments can be carried out in one round of optimization experiments, which accords with one parallel requirement of chemical reaction, and simultaneously reduces the requirement on professional literacy of testers and the cost required by the experiments. Therefore, the invention applies the Bayesian optimization algorithm to the project of optimizing the yield of the chemical reaction, and solves some problems existing in the traditional optimization method.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the interactive yield optimization method based on the Bayesian optimization algorithm, which can effectively improve the experimental efficiency of the chemical reaction yield optimization and bring great convenience to the research and development work of chemical experiments.
The technical scheme for solving the technical problems is as follows: a method for optimizing yield, said method comprising the steps of:
s1, preprocessing experimental data:
selecting reaction parameters (including substrates, solvents, ligands, temperature, reagents and the like) influencing the yield of the chemical reaction, dividing the reaction parameters into two major types of numerical type and non-numerical type, and then respectively encoding the numerical type and non-numerical type reaction parameters; for example, temperature, humidity, etc. are numerical reaction parameters, and solvent types are non-numerical reaction parameters.
S2, constructing a reaction parameter space:
carrying out Cartesian product combination operation on values corresponding to all reaction parameters to obtain all sets, wherein the sets are reaction parameter spaces; for example: the substrates comprise a, b and c; the ligand is A, B, C, and the parameter space is the combination of the substrate and the ligand in Cartesian product.
S3, constructing and optimizing a model
Selecting data in a reaction parameter space, performing a chemical reaction experiment to obtain yield data, constructing a model by using a Bayesian optimization algorithm, recommending a new round of experimental parameter combination, constructing an optimization model by using an experimental result obtained by the previous round of experimental parameter combination as priori knowledge, and recommending the experimental parameter combination of the next round;
and S4, carrying out specific experimental verification according to the experimental parameter combination recommended in the step S3, and carrying out multiple-round interaction with the algorithm in the step S3 to finally complete the yield optimization of the whole chemical reaction, thereby obtaining a satisfactory yield.
Further, in step S1, the numerical reaction parameter encoding mode is: the upper and lower limits of the numerical range are set by the experimenter for the numerical parameters, and the dividing scale of each numerical reaction parameter is defined at the same time.
Further, in step S1, the non-numerical reaction parameter encoding mode is: and inputting SMILES codes corresponding to each non-numerical reaction parameter, and combining one or more algorithms of a one-hot algorithm, a density functional theory algorithm (DFT) and a Mordred descriptor to obtain the coding value of the non-numerical reaction parameter.
Further, in step S2, when the reaction parameter space is constructed, redundant information generated during non-numerical reaction parameter coding, such as file name, mean value, variance, etc. generated by DFT algorithm is deleted first;
then, carrying out Cartesian product combination operation; the reaction parameter names in the reaction parameter space are then aligned with the encoded values using the pandas data processing tool.
Further, in step S3, the bayesian optimization algorithm includes a gaussian process and a selection function, and the bayesian optimization algorithm constructs a chemical reaction parameter model;
a priori knowledge model is built for the Bayesian optimization algorithm through a Gaussian process, the Bayesian optimization algorithm updates the original data set by taking the reaction parameter combination recommended in the previous round as new data in the algorithm interaction process, and the Gaussian process updates the priori knowledge model;
selecting a new round of sampling points by the selection function through the priori knowledge model, and calculating posterior distribution through the sampling points; at the same time, the selection of the optimal sampling point in the current area and the sampling point of the unknown area are balanced.
Further, the gaussian process formula is as follows:
f(x)～gp(m(x),k(x，x′))
wherein m (x) is a mean function, k (x, x ') is a covariance function, and x' are two groups of input parameters;
the selection function in the Bayesian optimization algorithm comprises: upper Confidence Bound (UCB) and Expected Improvement (EI).
Further, expected improvement selection functions are:
wherein,,phi (z) are a Gaussian distribution cumulative probability function and a probability density function, respectively, f (x+) represents the existing maximum value, and x+ takes f (x+) to the maximum value input parameter
Upper confidence bound selection function is:
UCB(x)＝μ(x)+kσ(x)
wherein μ (x) is the mean; sigma (x) is the standard deviation; k is the adjustment coefficient.
Further, the Bayesian optimization algorithm flow is as follows:
firstly, initializing the chemical reaction parameter model, and randomly selecting five groups of reaction parameter data from the reaction parameter space obtained in the step S2 as Gaussian process regression training data;
carrying out a specific chemical experiment by an experimenter according to initialized reaction parameter data, aligning the yield with each group of reaction parameter data after obtaining the yield, updating a data set, and establishing a priori knowledge model according to the updated data set in a Gaussian process;
then, a new batch of 4-6 groups of reaction parameter combinations are calculated through a priori knowledge model by selecting the function, and specific chemical experiments are carried out by experimenters to obtain the yield of each group of reaction parameter experiments;
finally, judging whether the current requirement is met or not by an experimenter according to the obtained yield data, and stopping the experiment if the current requirement is met; if the reaction parameters and the yield data of the specific chemical experiment are not met, updating the reaction parameters and the yield data of the specific chemical experiment to a data set, performing model optimization and calculation again, recommending a new batch of reaction parameter combinations, and repeating the experimental steps until the yield meets the requirements of experimental staff.
The invention also discloses a yield optimization system, which at least comprises a central processing unit (cpu) and a memory in communication connection with the cpu, wherein the cpu can execute the program of the yield optimization method, and the memory can store the program instructions and related parameter models which are called and executed by the cpu.
The invention also discloses a non-transitory computer readable storage medium storing computer instructions that cause a computer to perform the yield optimization method.
The beneficial effects of the invention are as follows:
the invention provides a method for optimizing the yield, which adopts an interactive experimental means by means of a Bayesian optimization algorithm to finally obtain a satisfactory reaction yield for experimental staff. The experimental efficiency of chemical reaction yield optimization can be effectively improved, and great convenience is brought to chemical experiment research and development work.
The method disclosed by the invention is an interactive yield optimization method based on a Bayesian optimization algorithm, supports parallel calculation of a plurality of experiments, can carry out a plurality of groups of experiments in one round of optimization experiments, meets one parallel requirement of chemical reaction, simultaneously reduces the professional literacy requirement of testers, and reduces the cost required by the experiments.
The yield optimization method can realize the processing calculation of the numerical reaction parameters and the processing calculation of the non-numerical reaction parameters, and realize the comprehensive processing of the numerical reaction parameters and the non-numerical reaction parameters, thereby providing comprehensive and comprehensive yield optimization conditions for experimenters, greatly improving the yield optimization efficiency of chemical experiments and reducing the labor intensity of experimenters.
Drawings
FIG. 1 is a flowchart of a Bayesian optimization algorithm in accordance with an embodiment;
FIG. 2 is a flow chart of experimental interactions in an embodiment.
Detailed Description
The following detailed description of the present invention will provide further details in order to make the above-mentioned objects, features and advantages of the present invention more comprehensible. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The present invention may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit of the invention, whereby the invention is not limited to the specific embodiments disclosed below.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
A method for optimizing yield, said method comprising the steps of:
s1, preprocessing experimental data:
reaction parameters (including substrates, solvents, ligands, temperatures, reagents, etc.) affecting the yield of the chemical reaction are selected, the reaction parameters are divided into two major classes of numeric and non-numeric, and then the numeric and non-numeric reaction parameters are encoded respectively. For example, temperature, humidity, etc. are numerical reaction parameters, and solvent types are non-numerical reaction parameters.
The numerical reaction parameter coding mode is as follows: the upper and lower limits of the numerical range are set by the experimenter for the numerical parameters, and the dividing scale of each numerical reaction parameter is defined at the same time. For example, temperature and humidity belong to numerical reaction parameters, each parameter of temperature and humidity is defined with a dividing scale, for example, the upper limit and the lower limit of the temperature are defined to be 10-20 ℃, the temperature can be divided into [10, 12, 14, 16, 18, 20] by an experimenter, the aim is to find out the specific temperature scale when the optimal state is reached in the experiment, and the numerical reaction parameters (temperature) are coded as follows:
temperatures [10, 12, 14, 16, 18, 20]
The non-numerical reaction parameter coding mode is as follows: and inputting SMILES codes corresponding to each non-numerical reaction parameter, and combining one or more algorithms of a one-hot algorithm, a density functional theory algorithm (DFT) and a Mordred descriptor to obtain the coding value of the non-numerical reaction parameter.
The three modes of one-hot algorithm, density functional theory algorithm (DFT) and mordred descriptor can be used in combination or alone, and the mode of use depends on an empirical assessment of the experiment by the experimenter and which coding can be performed on the compound represented by the SMILES code.
When the calculation force is enough, a DFT coding mode can be selected, and enough chemical mechanisms can be obtained as characteristics; the mordred algorithm can extract chemical mechanisms from SMILES as features; the one-hot algorithm extracts features from a purely mathematical perspective, and these features are used for modeling.
S2, constructing a reaction parameter space:
carrying out Cartesian product combination operation on the values corresponding to all the reaction parameters to obtain all the sets, namely a reaction parameter space; for example: the substrates comprise a, b and c; the ligand is A, B, C, and the parameter space is the combination of the substrate and the ligand in Cartesian product.
Such as non-numeric reaction parameters: the substrates comprise a, b and c; numerical reaction parameters: the temperature is 15 and 20; the parameter space contains the combination of: [a 15] [ a 20], [ b 15], [ b 20], [ c 15], [ c 20], i.e., a Cartesian combination.
When a reaction parameter space is constructed, firstly, redundant information generated during non-numerical reaction parameter coding, such as file name, mean value, variance and other redundant information generated by a DFT algorithm, is deleted; then, carrying out Cartesian product combination operation; the reaction parameter names in the reaction parameter space are then aligned with the encoded values using the pandas data processing tool.
S3, constructing and optimizing a model
Constructing a model by using a Bayesian optimization algorithm, and then constructing an optimization model by using an experimental result obtained by the experimental parameter combination of the previous round as priori knowledge and recommending the experimental parameter combination of the next round;
the Bayesian optimization algorithm comprises a Gaussian process and a selection function, and constructs a chemical reaction parameter model f (x) x-R, wherein x is a group of reaction parameter combinations in a reaction parameter space;
a priori knowledge model is built for the Bayesian optimization algorithm through a Gaussian process, the Bayesian optimization algorithm updates the original data set by taking the reaction parameter combination recommended in the previous round as new data in the algorithm interaction process, and the Gaussian process updates the priori knowledge model;
selecting a new round of sampling points by the selection function through the priori knowledge model, and calculating posterior distribution through the sampling points; at the same time, the selection of the optimal sampling point in the current area and the sampling point of the unknown area are balanced.
Further, the gaussian process formula is as follows:
f(x)～gp(m(x),k(x,x′))
wherein m (x) is a mean function and k (x, x') is a covariance function;
the selection function in the Bayesian optimization algorithm comprises: upper Confidence Bound (UCB) and Expected Improvement (EI).
Further, expected improvement selection functions are:
wherein,,phi (z) are a Gaussian distribution cumulative probability function and a probability density function, respectively, f (x+) represents the existing maximum value, and x+ takes f (x+) to the maximum value input parameter
Upper confidence bound selection function is:
UCB(x)＝μ(x)+kσ(x)
wherein μ (x) is the mean; sigma (x) is the standard deviation; k is an adjustment parameter.
And S4, carrying out specific experimental verification according to the experimental parameter combination recommended in the step S3, and carrying out multiple-round interaction with the algorithm in the step S3 to finally complete the yield optimization of the whole chemical reaction, thereby obtaining a satisfactory yield.
In this embodiment, the bayesian optimization algorithm flow is as follows:
s3-1, initializing the chemical reaction parameter model, and randomly selecting five groups of reaction parameter data from the reaction parameter space obtained in the step S2 as Gaussian process regression training data;
s3-2, carrying out a specific chemical experiment by an experimenter according to the initialized reaction parameter data, aligning the yield with each group of reaction parameter data after obtaining the yield, updating a data set, and establishing a priori knowledge model according to the updated data set in a Gaussian process;
s3-3, calculating a new batch of five groups of reaction parameter combinations by a selection function through a priori knowledge model, deleting the calculated five groups of reaction parameter combinations by experimenters according to experience knowledge in the field, adding a plurality of groups of reaction parameter combinations according to experience, determining the reaction parameter combination which finally needs to be subjected to a specific chemical experiment, and then carrying out the specific chemical experiment by the experimenters to obtain the yield of each group of reaction parameter experiment;
s3-4, judging whether the current requirement is met or not by an experimenter according to the obtained yield data, and stopping the experiment if the current requirement is met; if not, updating the reaction parameters and the yield data of the specific chemical experiment in the S3-3 to a data set, performing model optimization and calculation again, calculating a new batch of five groups of reaction parameter combinations, and repeating the experimental steps by combining the experience of the professional experimenter until the yield meets the requirement of the experimenter, as shown in figure 2.
A yield optimization system, said system having at least one central processing unit (cpu) and a memory in communication with the central processing unit, said central processing unit being operable to execute a program of said yield optimization method, said memory being operable to store program instructions and associated parametric models for execution by a call made by the central processing unit.
A non-transitory computer readable storage medium storing computer instructions that cause a computer to perform the yield optimization method.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (5)
1. A method for optimizing yield, characterized in that the method comprises the steps of:
s1, preprocessing experimental data:
selecting reaction parameters influencing the yield of chemical reaction, dividing the reaction parameters into two major types of numerical type and non-numerical type, and then respectively coding the numerical type and non-numerical type reaction parameters;
s2, constructing a reaction parameter space:
carrying out Cartesian product combination operation on the values corresponding to all the reaction parameters to obtain all the sets, namely a reaction parameter space;
s3, constructing and optimizing a model
Selecting data in a reaction parameter space, performing a chemical reaction experiment to obtain yield data, constructing a model by using a Bayesian optimization algorithm, recommending a new round of experimental parameter combination, constructing an optimization model by using an experimental result obtained by the previous round of experimental parameter combination as priori knowledge, and recommending the experimental parameter combination of the next round;
s4, carrying out specific experimental verification according to the experimental parameter combination recommended in the step S3, and carrying out multi-round interaction with the algorithm in the step S3 to finally complete the yield optimization of the whole chemical reaction, thereby obtaining a satisfactory yield;
in step S1, the non-numerical reaction parameter coding mode is as follows: inputting SMILES codes corresponding to each non-numerical reaction parameter, and combining one or more algorithms of a one-hot algorithm, a density functional theory algorithm and a morred descriptor to obtain the coding value of the non-numerical reaction parameter;
in the step S3, a Bayesian optimization algorithm comprises a Gaussian process and a selection function, and the Bayesian optimization algorithm constructs a chemical reaction parameter model; a priori knowledge model is built for the Bayesian optimization algorithm through a Gaussian process, the Bayesian optimization algorithm updates the original data set by taking the reaction parameter combination recommended in the previous round as new data in the algorithm interaction process, and the Gaussian process updates the priori knowledge model; selecting a new round of sampling points by the selection function through the priori knowledge model, and calculating posterior distribution through the sampling points; meanwhile, the selection of the optimal sampling point in the current area and the selection of the sampling point of the unknown area are balanced;
the Bayesian optimization algorithm flow is as follows:
firstly, initializing the chemical reaction parameter model, and randomly selecting five groups of reaction parameter data from the reaction parameter space obtained in the step S2 as Gaussian process regression training data;
carrying out a specific chemical experiment by an experimenter according to initialized reaction parameter data, aligning the yield with each group of reaction parameter data after obtaining the yield, updating a data set, and establishing a priori knowledge model according to the updated data set in a Gaussian process;
then, a new batch of 4-6 groups of reaction parameter combinations are calculated through a priori knowledge model by selecting the function, and specific chemical experiments are carried out by experimenters to obtain the yield of each group of reaction parameter experiments;
finally, judging whether the current requirement is met or not by an experimenter according to the obtained yield data, and stopping the experiment if the current requirement is met; if the reaction parameters and the yield data of the specific chemical experiment are not met, updating the reaction parameters and the yield data of the specific chemical experiment to a data set, performing model optimization and calculation again, recommending a new batch of reaction parameter combinations, and repeating the experimental steps until the yield meets the requirements of experimental staff.
2. The method according to claim 1, wherein in step S1, the numerical reaction parameter encoding mode is: the upper and lower limits of the numerical range are set by the experimenter for the numerical parameters, and the dividing scale of each numerical reaction parameter is defined at the same time.
3. The method according to claim 1, wherein in step S2, when constructing the reaction parameter space, redundant information generated during encoding of the non-numeric reaction parameters is first deleted; then, carrying out Cartesian product combination operation; the reaction parameter names in the reaction parameter space are then aligned with the encoded values using the pandas data processing tool.
4. A yield optimization system, characterized in that the system has at least one central processor and a memory in communication with the central processor, the central processor being capable of executing a program of the yield optimization method according to any of claims 1-3, the memory being capable of storing program instructions and associated parametric models that are invoked for execution by the central processor.
5. A non-transitory computer readable storage medium storing computer instructions that cause a computer to perform the yield optimization method of any one of claims 1-3.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020087281A1 (en) * | 2018-10-30 | 2020-05-07 | 深圳市大疆创新科技有限公司 | Hyper-parameter optimization method and apparatus |
| GB202100758D0 (en) * | 2021-01-20 | 2021-03-03 | Jems Energetska Druzba D O O | Systems and methods for plant process optimisation |
| WO2022058113A1 (en) * | 2020-09-18 | 2022-03-24 | Bystronic Laser Ag | Computer implemented method of and optimisation tool for refinement of laser cutting process parameters by means of an optimization tool |
| CN114912342A (en) * | 2022-03-28 | 2022-08-16 | 南京邮电大学 | Packaging lead bonding process parameter optimization method based on multiple quality parameters |
| CN115129982A (en) * | 2022-06-22 | 2022-09-30 | 电计科技发展(上海)有限公司 | Experiment parameter recommendation method, device, terminal and medium based on improved Bayesian optimization |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021007812A1 (en) * | 2019-07-17 | 2021-01-21 | 深圳大学 | Deep neural network hyperparameter optimization method, electronic device and storage medium |
| CN111898212B (en) * | 2020-06-17 | 2022-12-09 | 西安交通大学 | Impeller mechanical profile design optimization method based on BezierGAN and Bayesian optimization |
| CN114781280B (en) * | 2022-06-21 | 2022-09-30 | 烟台国工智能科技有限公司 | Interactive yield optimization method and system based on recurrent neural network |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020087281A1 (en) * | 2018-10-30 | 2020-05-07 | 深圳市大疆创新科技有限公司 | Hyper-parameter optimization method and apparatus |
| WO2022058113A1 (en) * | 2020-09-18 | 2022-03-24 | Bystronic Laser Ag | Computer implemented method of and optimisation tool for refinement of laser cutting process parameters by means of an optimization tool |
| GB202100758D0 (en) * | 2021-01-20 | 2021-03-03 | Jems Energetska Druzba D O O | Systems and methods for plant process optimisation |
| CN114912342A (en) * | 2022-03-28 | 2022-08-16 | 南京邮电大学 | Packaging lead bonding process parameter optimization method based on multiple quality parameters |
| CN115129982A (en) * | 2022-06-22 | 2022-09-30 | 电计科技发展(上海)有限公司 | Experiment parameter recommendation method, device, terminal and medium based on improved Bayesian optimization |
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