High‐Throughput Generation of 3D Graphene Metamaterials and Property Quantification Using Machine Learning
3D graphene assemblies are proposed as solutions to meet the goal toward efficient utilization of 2D graphene sheets, showing excellent performances in applications such as mechanical support, energy storage, and electrochemical catalysis. However, given the diversity and complexity of possible grap...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022
|
| Online Access: | https://hdl.handle.net/1721.1/145464 |
| _version_ | 1826213288733573120 |
|---|---|
| author | Yang, Zhenze Buehler, Markus J |
| author2 | Massachusetts Institute of Technology. Department of Materials Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Materials Science and Engineering Yang, Zhenze Buehler, Markus J |
| author_sort | Yang, Zhenze |
| collection | MIT |
| description | 3D graphene assemblies are proposed as solutions to meet the goal toward efficient utilization of 2D graphene sheets, showing excellent performances in applications such as mechanical support, energy storage, and electrochemical catalysis. However, given the diversity and complexity of possible graphene 3D structures, there does not yet exist a systematic approach that can generate target 3D shapes and also, evaluate their performance. Here high-throughput data generation is combined with artificial intelligence approaches to realize rapid structure formation and property quantification of 3D graphene foams with mathematically controlled topologies, driven by molecular dynamics simulations. More than 4000 different foam structures are created, which feature diverse topologies that contain potential pathways for small molecules and auxetic structures with negative Poisson's ratio. Empowered by machine learning (ML) algorithms including graph neural networks, not only global properties such as elastic moduli, but also local behaviors such as atomic stress can be predicted and optimized based on their atomic structure, bypassing expensive atomistic simulations. The key findings of the research reported in this paper include a high-throughput virtual framework of generating diverse 3D graphene assemblies with mechanical performances quantification, and highly efficient methods of evaluating physical properties based on ML. |
| first_indexed | 2024-09-23T15:46:42Z |
| format | Article |
| id | mit-1721.1/145464 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:46:42Z |
| publishDate | 2022 |
| publisher | Wiley |
| record_format | dspace |
| spelling | mit-1721.1/1454642022-10-02T04:02:11Z High‐Throughput Generation of 3D Graphene Metamaterials and Property Quantification Using Machine Learning Yang, Zhenze Buehler, Markus J Massachusetts Institute of Technology. Department of Materials Science and Engineering Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics Massachusetts Institute of Technology. Center for Computational Science and Engineering 3D graphene assemblies are proposed as solutions to meet the goal toward efficient utilization of 2D graphene sheets, showing excellent performances in applications such as mechanical support, energy storage, and electrochemical catalysis. However, given the diversity and complexity of possible graphene 3D structures, there does not yet exist a systematic approach that can generate target 3D shapes and also, evaluate their performance. Here high-throughput data generation is combined with artificial intelligence approaches to realize rapid structure formation and property quantification of 3D graphene foams with mathematically controlled topologies, driven by molecular dynamics simulations. More than 4000 different foam structures are created, which feature diverse topologies that contain potential pathways for small molecules and auxetic structures with negative Poisson's ratio. Empowered by machine learning (ML) algorithms including graph neural networks, not only global properties such as elastic moduli, but also local behaviors such as atomic stress can be predicted and optimized based on their atomic structure, bypassing expensive atomistic simulations. The key findings of the research reported in this paper include a high-throughput virtual framework of generating diverse 3D graphene assemblies with mechanical performances quantification, and highly efficient methods of evaluating physical properties based on ML. 2022-09-16T15:28:40Z 2022-09-16T15:28:40Z 2022-07-29 2022-09-16T15:18:02Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/145464 Yang, Zhenze and Buehler, Markus J. 2022. "High‐Throughput Generation of 3D Graphene Metamaterials and Property Quantification Using Machine Learning." Small Methods. en 10.1002/smtd.202200537 Small Methods Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Wiley Wiley |
| spellingShingle | Yang, Zhenze Buehler, Markus J High‐Throughput Generation of 3D Graphene Metamaterials and Property Quantification Using Machine Learning |
| title | High‐Throughput Generation of 3D Graphene Metamaterials and Property Quantification Using Machine Learning |
| title_full | High‐Throughput Generation of 3D Graphene Metamaterials and Property Quantification Using Machine Learning |
| title_fullStr | High‐Throughput Generation of 3D Graphene Metamaterials and Property Quantification Using Machine Learning |
| title_full_unstemmed | High‐Throughput Generation of 3D Graphene Metamaterials and Property Quantification Using Machine Learning |
| title_short | High‐Throughput Generation of 3D Graphene Metamaterials and Property Quantification Using Machine Learning |
| title_sort | high throughput generation of 3d graphene metamaterials and property quantification using machine learning |
| url | https://hdl.handle.net/1721.1/145464 |
| work_keys_str_mv | AT yangzhenze highthroughputgenerationof3dgraphenemetamaterialsandpropertyquantificationusingmachinelearning AT buehlermarkusj highthroughputgenerationof3dgraphenemetamaterialsandpropertyquantificationusingmachinelearning |