CN108763764B - Calculation method for buckling deformation of graphene-flexible substrate structure - Google Patents

Calculation method for buckling deformation of graphene-flexible substrate structure Download PDF

Info

Publication number
CN108763764B
CN108763764B CN201810537544.XA CN201810537544A CN108763764B CN 108763764 B CN108763764 B CN 108763764B CN 201810537544 A CN201810537544 A CN 201810537544A CN 108763764 B CN108763764 B CN 108763764B
Authority
CN
China
Prior art keywords
graphene
energy
elastic
pdms
calculating
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
Application number
CN201810537544.XA
Other languages
Chinese (zh)
Other versions
CN108763764A (en
Inventor
高希光
袁雨浓
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Aeronautics and Astronautics
Original Assignee
Nanjing University of Aeronautics and Astronautics
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Nanjing University of Aeronautics and Astronautics filed Critical Nanjing University of Aeronautics and Astronautics
Priority to CN201810537544.XA priority Critical patent/CN108763764B/en
Publication of CN108763764A publication Critical patent/CN108763764A/en
Application granted granted Critical
Publication of CN108763764B publication Critical patent/CN108763764B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming

Abstract

A method for calculating buckling deformation of a graphene-flexible substrate structure comprises the following steps: establishing a graphene atom model; establishing an atomic model of the flexible substrate; calculating the total potential energy of the graphene and the elastic substrate thereof; and seeking the minimum potential energy according to the calculation of the total potential energy. The method is combined with the genetic algorithm to carry out optimization calculation, so that the numerical precision of the result is improved, the calculation time is greatly shortened, and the similar and larger-size sample simulation calculation is facilitated in the future.

Description

Calculation method for buckling deformation of graphene-flexible substrate structure
Technical Field
The invention belongs to the technical field of micro-nano composite materials, and particularly relates to a method for calculating buckling deformation of a graphene-flexible substrate structure.
Background
Graphene (Graphene) is a cellular planar thin film formed by carbon atoms in an sp2 hybridization manner, and is a quasi-two-dimensional material with a thickness of only one atomic layer. Due to its excellent strength, flexibility, electrical conductivity, thermal conductivity and optical properties, it has been developed in the fields of physics, materials science, electronic information, computer, aerospace and the like. The graphene thin film produced by the chemical vapor deposition method releases after the tensile strain reaches 40%, is plastically deformed by less than 0.2%, and does not cause any damage to the inside, which makes it a potential stretchable electrode material of an electrical energy storage device. Because the buckling form of the graphene depends on the graphene, the strength of the substrate of the graphene and the surface interaction between the graphene and the substrate, the establishment of a good physical model and the calculation numerical analysis are greatly helpful for better understanding of a graphene substrate system, the improvement of the performance of the graphene equipment and the confirmation of key mechanical parameters of the graphene equipment. The main analytical and modeling methods include molecular dynamics, classical continuous medium mechanics, and molecular mechanics, of which molecular dynamics and classical continuous medium mechanics are the two most dominant methods today.
Zheng (q.zheng, y.geng, s.wang, z.li and j.k.kim, Effects of functional groups on the mechanical and warping properties of graphene sheets, CARBON 48(2010), 4315-. Zhu (S.Zhu and T.Li, training nature of Graphene on Substrate-Supported Nanoparticles, Journal of Applied Mechanics 81 (2014)), 61008, shows that Graphene materials have adhesion to their nano-substrates, which is caused by van der Waals forces. When the monolayer graphene and the graphene oxide are researched by Shen (x.shen, x.lin, n.yousefi, j.jia and j.k.kim, wrinking in graphene sheets and graphene oxide sheets, CARBON 66(2014), 84-92), the interaction between oxygen atoms at the edge is found to generate an effect on the buckling of the graphene, and the oxygen atoms on the surface hardly generate an effect on the buckling due to the stable low thermodynamic energy of the oxygen atoms. Liu (F.Liu, N.Hu, H.Ning, Y.Liu, Y.Li and L.Wu, Molecular dynamics simulation on interfacial mechanical properties of polymer nanocomposites with curved graphene, COMP MATER SCI 108(2015), 160-167.) also found that graphene is affected by the substrate to generate buckling.
Even though many researchers have done a lot of work before using molecular dynamics, the graphene-substrate model built still suffers from limitations on length and time scales. Therefore, the continuous medium mechanics theory is more suitable for analyzing graphene-substrate systems compared to molecular dynamics. Zhang (K.Zhang and M.Arroyo, underlying and structural wire networks in supported graphs through relationships, Journal of the Mechanics & Physics of Solids 72(2014), 61-74.) investigated the effect of tensile, adhesive and frictional forces in different directions on the deformation of buckling by numerical simulations of continuous medium theory. Steven uses the L-J3-9 potential function to simulate interlayer adhesion. When the substrate is not perfectly flat, the morphology of the substrate can also affect the morphology of the graphene. These theoretical results suggest that morphological changes of graphene can be controlled by changing the surface shape and strain of the oxygen-containing substrate. However, how to calculate the van der waals force between interfaces in the continuous medium theory remains a challenge.
Recently, Gao (x.gao, c.li, y.song and t.w.chou, a continuous mechanics model of multi-bundling in graphene-substrate systems with random distributed partitioning, International Journal of Solids & Structures 97-98(2016), 510-. However, in the process of calculating the simulation, the energy minimum value is obtained by using a traversal algorithm, and the calculation period is long in the process of searching for the energy minimum value.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for calculating the buckling deformation of a graphene-flexible substrate structure.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for calculating buckling deformation of a graphene-flexible substrate structure is characterized by comprising the following steps:
step one, establishing a graphene atom model;
secondly, establishing an atomic model of the flexible substrate;
calculating the total potential energy of the graphene and the elastic substrate thereof;
and step four, seeking the minimum potential energy according to the calculation of the total potential energy.
In order to optimize the technical scheme, the specific measures adopted further comprise:
in the first step, the graphene atomic model is constructed by taking square Bravais lattices as a repeating unit, and the number of the carbon rings in the transverse direction and the longitudinal direction depends on the length of the carbon rings in the transverse direction and the longitudinal direction.
In the second step, a PDMS chain is established, and the chain is translated and rotated to form the whole PDMS body.
The PDMS chain was 10 units long.
The third step specifically comprises:
calculating the elastic energy of the graphene and the elastic substrate thereof by using the large deformation energy:
Figure BDA0001676527820000021
wherein, Ug_elasticThe elastic energy of graphene is represented, w represents the width of a graphene unit, E represents the elastic modulus, l represents the length of the graphene unit, h represents the height of the graphene unit, epsilon represents the strain received in the x direction, and z and x represent the z direction and the x direction respectively;
the PDMS substrate was divided into planar unit cells, and the energies of the planar unit cells were summed up as a sum:
Figure BDA0001676527820000031
wherein, Updms_elasticWhich represents the elastic energy of the PDMS,
Figure BDA0001676527820000032
representing elastic energy of PDMS unit body;
the Lennard-Jones potential function was used to fit and calculate the van der waals energy of the atoms between graphene and its substrate:
Figure BDA0001676527820000033
Uvdw＝∑Uij
wherein, UijDenotes Van der Waals energy between the ith and jth atoms, r is the distance between the ith and jth atoms, UvdwIs the van der waals potential energy of the whole structure;
Figure BDA0001676527820000034
wherein r isiIs the atomic distance of the ith atom, rjIs the atomic distance of the jth atom, εiIs the depth of the potential well of the ith atom,. epsilonjIs the potential well depth of the jth atom;
the total potential energy is then:
U＝Ug_elastic+Updms_elastic+Uvdw
wherein U represents the total potential energy of graphene and its elastic substrate.
In the fourth step, based on the calculation of total potential energy, the energy of the graphene PDMS substrate system is optimized and calculated by using a genetic algorithm, and the A with the lowest energy value is searchedg、Asλ and h, wherein AgDenotes the amplitude of graphene, AsRepresents the amplitude of the PDMS and λ represents the wavelength.
In search of the lowest energy value of Ag、AsAnd when lambda and h are carried out, randomly generating 2n groups of individuals as initial individuals, wherein n is a natural number, respectively calculating the energy value of each individual, sequencing the 2n groups of individuals from small to large by using a rapid sequencing method, and selecting n groups of individuals with the minimum energy to obtain Ag、AsThe lambda and h values are reserved as a parent, the n groups of individuals are crossed and mutated to form new n generations of offspring, the new n generations of offspring and the original n groups of individuals of the parent form a new population, the new population is subjected to energy calculation and sequencing, the processes are repeated by iteration until the individuals in the population are completely consistent or the maximum number of iterations is reached, the minimum energy value is the same as Ag、Asλ and h.
The invention has the beneficial effects that: by combining the genetic algorithm to carry out optimization calculation, the numerical precision of the result is improved, and the calculation time is greatly shortened, which is beneficial to carrying out similar sample simulation calculation with larger size in the future.
Drawings
Fig. 1 is a schematic diagram of a graphene atomic model.
FIG. 2 is an atomic model schematic of 10 unit chain length for a flexible substrate.
Fig. 3 is a schematic flow chart of seeking a minimum potential energy.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings.
Based on the process of stretching and releasing, a PDMS (polydimethylsiloxane) substrate is first given a tensile stress epsilon0Then attaching the graphene to the substrate, releasing the substrate,the graphene is thus subjected to a compressive force due to the rebound of the substrate.
The most important problem in classical continuous mechanics is how to directly obtain the size of the interaction between the substrate and graphene by performing numerical calculation on the interface interaction between the graphene-substrate system. By constructing the atomic structure model, the problem can be solved effectively.
The boundary condition is assumed that both sides of x ═ 0 are completely symmetrical planes, and when x ═ 0, the displacement in the x direction is also 0, and the displacement in the z direction of the lowermost layer of PDMS is also assumed to be 0. Although both graphene and PDMS have certain widths, we assume that they do not produce any displacement deformation in the y direction, but only in the x and z directions. The deformation of graphene and its base beam element is therefore analysed using the plane strain assumption.
Thus, the overall system energy can be divided into three parts: the first part is the elastic energy of graphene consisting of sp2 hybridized carbon atoms; the second part is the elastic energy of the polymer consisting of PDMS molecules; the third part is the van der waals energy between the graphene and the substrate PDMS.
The method comprises the following steps: establishing a graphene atomic model
The carbon rings are constructed by using simple square Bravais lattices as repeating units, and the number of the carbon rings in the transverse direction and the longitudinal direction depends on the length of the carbon rings in the transverse direction and the longitudinal direction, as shown in FIG. 1.
Step two: atomic model for creating flexible substrates
It is very difficult to construct a model accurately because of the defects in the polymer and the change in bond angle and bond length of the atoms in the polymer due to the action of the surrounding atoms. Therefore, a 10-unit-long PDMS chain is first established, and then the chain is translated and rotated to form the whole PDMS body, as shown in fig. 2.
Step three: calculating total potential energy
Calculating the elastic energy of the graphene and the elastic substrate thereof by using the large deformation energy:
Figure BDA0001676527820000051
wherein, Ug_elasticDenotes the elastic energy of graphene, w denotes the width of a graphene unit, E denotes the elastic modulus, l denotes the length of the graphene unit, h denotes the height of the graphene unit, E denotes the strain received in the x direction, and z and x denote the z and x directions, respectively.
By dividing the PDMS substrate into planar unit cells and adding their energies as a sum:
Figure BDA0001676527820000052
wherein, Updms_nselasticWhich represents the elastic energy of the PDMS,
Figure BDA0001676527820000053
representing the elastic behavior of the PDMS unit body.
And calculating the subsequent unit energy by the same method as the large deformation elastic energy of the graphene.
The Lennard-Jones potential function was used to fit and calculate the van der waals energy of the atoms between graphene and its substrate:
Figure BDA0001676527820000055
Uvdw＝∑Uij
wherein, UijDenotes Van der Waals energy between the ith and jth atoms, r is the distance between the ith and jth atoms, UvdwIs the van der waals potential of the structure as a whole.
Figure BDA0001676527820000054
Wherein r isiIs the atomic distance of the ith atom, rjIs the atomic distance of the jth atom, εiIs the depth of the potential well of the ith atom,. epsiloniIs the potential well depth of the jth atom.
The total potential energy is then:
U＝Ug_elastic+Updms_elastic+Uvdw
wherein U represents the total potential energy of graphene and its elastic substrate.
Step four: seeking minimum potential energy
And (4) based on the calculation of the total potential energy, optimizing and calculating the energy of the graphene PDMS substrate system by using a genetic algorithm. As shown in FIG. 3, A is being sought for the lowest energy valueg、AsAnd when lambda and h are used, randomly generating 2n groups of individuals as initial individuals, respectively calculating the energy value of each individual, sequencing the 2n groups of individuals from small to large by using a quick sequencing method, selecting n groups of individuals with the minimum energy, and carrying out A on the individualsg、AsThe lambda and h values are reserved as parents, the n groups of individuals are crossed and mutated according to certain probability to form new n generations of offspring, the new n generations of offspring and the original n groups of individuals of the parents form a new population, the new population is subjected to energy calculation and sequencing, the processes are repeated in a repeated iteration mode until the individuals in the population are completely consistent or the maximum number of iterations is reached, the minimum energy value is the same as Ag、Asλ and h. Wherein Ag is graphene amplitude, As is PDMS amplitude, lambda is wavelength, and n is a natural number, and can be set As required.
Example one
In the simulation calculation, the initial tensile strain epsilon040% strain after relaxation is εr0; PDMS had an initial length of 19.6241nm, a width of 3.9704nm, a height of 4nm and a density of 0.8X 103kg/m3The elastic modulus is 2.63 MPa; the elastic modulus and thickness of graphene were 1.1TPa and 0.335nm, respectively, the total number of initial parents was set as 20 groups, the number of evolutions was set as 50, and the crossover and mutation probabilities were set as 0.6 and 0.1.
Initial setting of Ag、AsLambda and h genetic search ranges of [0, 3.9%]nm、[0，3.9]nm、[10，300]nm and [0.01, 0.4 ]]nm, A finally searched out by computer simulationg、Asλ and h are each Ag＝1.1918nm，As0.1006nm, 115.9660nm and h 0.3000nm, with an energy minimum of 8.406 × 10-4pJ, which is 1.636X 10 times its energy when it is completely flat-3pJ is small, and the whole result operation time is about 90 minutes.
Compared with the previous traversal algorithm, the method searches A through computer simulation under the same search range and initial conditiong、Asλ and h are each Ag＝1.2nm，AsThe total calculation time is up to 6 hours, with λ 120nm and h 0.3 nm. The operation time is greatly shortened by utilizing the genetic algorithm, the whole result operation time only needs 90 minutes, but the result tends to be consistent and even more accurate compared with the traversal algorithm.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.

Claims (4)

1. A method for calculating buckling deformation of a graphene-flexible substrate structure is characterized by comprising the following steps:
step one, establishing a graphene atom model; in the first step, a graphene atomic model is constructed by taking square Bravais lattices as a repeating unit, and the number of the carbon rings in the transverse direction and the longitudinal direction depends on the length of the carbon rings in the transverse direction and the longitudinal direction;
secondly, establishing an atomic model of the flexible substrate; in the second step, a PDMS chain is established, and the chain is translated and rotated to form the whole PDMS body;
calculating the total potential energy of the graphene and the elastic substrate thereof; the third step specifically comprises:
calculating the elastic energy of the graphene and the elastic substrate thereof by using the large deformation energy:
Figure FDA0003487733100000011
wherein, Ug_elasticThe elastic energy of graphene is represented, w represents the width of a graphene unit, E represents the elastic modulus, l represents the length of the graphene unit, h represents the height of the graphene unit, epsilon represents the strain received in the x direction, and z and x represent the z direction and the x direction respectively;
the PDMS substrate was divided into planar unit cells, and the energies of the planar unit cells were summed up as a sum:
Figure FDA0003487733100000012
wherein, Updms_elasticWhich represents the elastic energy of the PDMS,
Figure FDA0003487733100000013
representing elastic energy of PDMS unit body;
the Lennard-Jones potential function was used to fit and calculate the van der waals energy of the atoms between graphene and its substrate:
Figure FDA0003487733100000014
Uvdw＝∑Uij
wherein, UijDenotes Van der Waals energy between the ith and jth atoms, r is the distance between the ith and jth atoms, UvdwIs the van der waals potential energy of the whole structure;
Figure FDA0003487733100000015
wherein r isiIs the atomic distance of the ith atom, rjIs the atomic distance of the jth atom, εiIs the depth of the potential well of the ith atom,. epsilonjIs the potential well depth of the jth atom;
the total potential energy is then:
U＝Ug_elastic+Updms_elastic+Uvdw
wherein U represents the total potential energy of graphene and its elastic substrate;
and step four, seeking the minimum potential energy according to the calculation of the total potential energy.
2. The method for calculating the buckling deformation of the graphene-flexible substrate structure as claimed in claim 1, wherein: the PDMS chain was 10 units long.
3. The method for calculating the buckling deformation of the graphene-flexible substrate structure as claimed in claim 1, wherein: in the fourth step, based on the calculation of total potential energy, the energy of the graphene PDMS substrate system is optimized and calculated by using a genetic algorithm, and the A with the lowest energy value is searchedg、Asλ and h, wherein AgDenotes the amplitude of graphene, AsRepresents the amplitude of the PDMS and λ represents the wavelength.
4. The method for calculating the buckling deformation of the graphene-flexible substrate structure according to claim 3, wherein: in search of the lowest energy value of Ag、AsAnd when lambda and h are carried out, randomly generating 2n groups of individuals as initial individuals, wherein n is a natural number, respectively calculating the energy value of each individual, sequencing the 2n groups of individuals from small to large by using a rapid sequencing method, and selecting n groups of individuals with the minimum energy to obtain Ag、AsThe lambda and h values are reserved as a parent, the n groups of individuals are crossed and mutated to form new n generations of offspring, the new n generations of offspring and the original n groups of individuals of the parent form a new population, the new population is subjected to energy calculation and sequencing, the processes are repeated by iteration until the individuals in the population are completely consistent or the maximum number of iterations is reached, the minimum energy value is the same as Ag、Asλ and h.
CN201810537544.XA 2018-05-29 2018-05-29 Calculation method for buckling deformation of graphene-flexible substrate structure Active CN108763764B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810537544.XA CN108763764B (en) 2018-05-29 2018-05-29 Calculation method for buckling deformation of graphene-flexible substrate structure

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810537544.XA CN108763764B (en) 2018-05-29 2018-05-29 Calculation method for buckling deformation of graphene-flexible substrate structure

Publications (2)

Publication Number Publication Date
CN108763764A CN108763764A (en) 2018-11-06
CN108763764B true CN108763764B (en) 2022-04-15

Family

ID=64004240

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810537544.XA Active CN108763764B (en) 2018-05-29 2018-05-29 Calculation method for buckling deformation of graphene-flexible substrate structure

Country Status (1)

Country Link
CN (1) CN108763764B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109472113B (en) * 2018-12-10 2023-05-23 东南大学 Dynamic model analysis method for flexible MEMS electrostatic drive switch mechanics
CN111081323A (en) * 2019-12-19 2020-04-28 哈尔滨工业大学 Graphene multi-stage coarse granulation method based on Tersofff force field

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2010091405A2 (en) * 2009-02-09 2010-08-12 Applied Materials, Inc. Metrology methods and apparatus for nanomaterial characterization of energy storage electrode structures
CN103265013A (en) * 2013-04-26 2013-08-28 华中科技大学 Preparation methods of flexible substrate-based graphene film and flexible substrate-based graphene composite film
CN104616826A (en) * 2014-12-30 2015-05-13 浙江大学 Graphene-PDMS (Polydimethylsiloxane) flexible electrode and preparation method and application thereof
CN105449067A (en) * 2015-12-31 2016-03-30 白德旭 Graphene LED chip and preparation method thereof
CN106082186A (en) * 2016-06-12 2016-11-09 南京航空航天大学 A kind of heat conduction thin film of graphene nano carbon/carbon-copper composite material and preparation method thereof
CN107014799A (en) * 2017-03-24 2017-08-04 山东师范大学 A kind of graphene/silver nano flower-like/PMMA " sandwich " structural flexibility SERS substrates and preparation method thereof

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2010091405A2 (en) * 2009-02-09 2010-08-12 Applied Materials, Inc. Metrology methods and apparatus for nanomaterial characterization of energy storage electrode structures
CN103265013A (en) * 2013-04-26 2013-08-28 华中科技大学 Preparation methods of flexible substrate-based graphene film and flexible substrate-based graphene composite film
CN104616826A (en) * 2014-12-30 2015-05-13 浙江大学 Graphene-PDMS (Polydimethylsiloxane) flexible electrode and preparation method and application thereof
CN105449067A (en) * 2015-12-31 2016-03-30 白德旭 Graphene LED chip and preparation method thereof
CN106082186A (en) * 2016-06-12 2016-11-09 南京航空航天大学 A kind of heat conduction thin film of graphene nano carbon/carbon-copper composite material and preparation method thereof
CN107014799A (en) * 2017-03-24 2017-08-04 山东师范大学 A kind of graphene/silver nano flower-like/PMMA " sandwich " structural flexibility SERS substrates and preparation method thereof

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
弯曲应力对二维石墨烯导电性能的影响;傅凯;《应用科学学报》;20160331;第163-170页 *

Also Published As

Publication number Publication date
CN108763764A (en) 2018-11-06

Similar Documents

Publication Publication Date Title
Qin et al. The mechanics and design of a lightweight three-dimensional graphene assembly
Chandra et al. Advances in finite element modelling of graphene and associated nanostructures
Vijayaraghavan et al. Measurement of properties of graphene sheets subjected to drilling operation using computer simulation
Wei et al. Perspective: Predicting and optimizing thermal transport properties with machine learning methods
CN108763764B (en) Calculation method for buckling deformation of graphene-flexible substrate structure
Liu et al. Surrogate models in machine learning for computational stochastic multi-scale modelling in composite materials design
Mrozek et al. Nano level optimization of graphene allotropes by means of a hybrid parallel evolutionary algorithm
Huang et al. Adsorption and modification behavior of single atoms on the surface of single vacancy graphene: Machine learning accelerated first principle computations
Gupta et al. Probing the stochastic fracture behavior of twisted bilayer graphene: Efficient ANN based molecular dynamics simulations for complete probabilistic characterization
Wei et al. SISSO-assisted prediction and design of mechanical properties of porous graphene with a uniform nanopore array
Varma Siruvuri et al. Fracture strength of Graphene at high temperatures: data driven investigations supported by MD and analytical approaches
Mrozek et al. Examination of mechanical properties of graphene allotropes by means of computer simulation
El Said Predicting the non-linear response of composite materials using deep recurrent convolutional neural networks
Mrozek Basic mechanical properties of 2h and 1t single-layer molybdenum disulfide polymorphs. a short comparison of various atomic potentials
Kojima et al. Search strategy for rare microstructure to optimize material properties of filled rubber using machine learning based simulation
Cranford et al. Mechanomutable carbon nanotube arrays
Rashid et al. Revealing the predictive power of neural operators for strain evolution in digital composites
Garg et al. Application of artificial intelligence technique for modelling elastic properties of 2D nanoscale material
Adetiba et al. Classification of eukaryotic organisms through cepstral analysis of mitochondrial DNA
Wang et al. Thermal boundary resistance at graphene-pentacene interface explored by a data-intensive approach
Dirrenberger et al. Effective properties of architectured materials
Vijayaraghavan et al. Multiphysics-Based Statistical Model for Investigating the Mechanics of Carbon Nanotubes Membranes for Proton-Exchange Membrane Fuel Cell Applications
Vajire et al. Effect of porosity on the stress–strain response of aluminium nanocomposites: a multiscale approach
Wei et al. Identifying the elastic isotropy of architectured materials based on deep learning method
Bian et al. A phase-field based finite element method for modeling graphene flake reinforced composites

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