Bibliographic Details
| Title: |
Machine learning meets quantum mechanics in catalysis |
| Authors: |
James P. Lewis, Pengju Ren, Xiaodong Wen, Yongwang Li, Guanhua Chen |
| Source: |
Frontiers in Quantum Science and Technology, Vol 2 (2023) |
| Publisher Information: |
Frontiers Media S.A., 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Technology |
| Subject Terms: |
machine learning, catalysts, high-throughput, reaction coordinates, structure-function relationships, Technology |
| More Details: |
Over the past decade many researchers have applied machine learning algorithms with computational chemistry and materials science tools to explore properties of catalysts. There is a rapid increase in publications demonstrating the use of machine learning for rational catalyst design. In our perspective, targeted tools for rational catalyst design will continue to make significant contributions. However, the community should focus on developing high-throughput simulation tools that utilize molecular dynamics capabilities for thorough exploration of the complex potential energy surfaces that exist, particularly in heterogeneous catalysis. Catalyst-specific databases should be developed to contain enough data to represent the complex multi-dimensional space that defines structure-function relationships. Machine learning tools will continue to impact rational catalyst design; however, we believe that more sophisticated pattern recognition algorithms would yield better understanding of structure-function relationships for heterogeneous catalysis. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2813-2181 |
| Relation: |
https://www.frontiersin.org/articles/10.3389/frqst.2023.1232903/full; https://doaj.org/toc/2813-2181 |
| DOI: |
10.3389/frqst.2023.1232903 |
| Access URL: |
https://doaj.org/article/316a4d92baab4557b0a9193486e83a56 |
| Accession Number: |
edsdoj.316a4d92baab4557b0a9193486e83a56 |
| Database: |
Directory of Open Access Journals |
