Minimizing Redundancy and Data Requirements of Machine Learning Potential: A Case Study in Interface Combustion-Reference-Cited by-同舟云学术

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Minimizing Redundancy and Data Requirements of Machine Learning Potential: A Case Study in Interface Combustion

Published:2024-07-29 Issue:15 Volume:20 Page:6813-6825
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Chang Xiaoya¹^ORCID,Zhang Di¹,Chu Qingzhao¹^ORCID,Chen Dongping¹^ORCID

Affiliation:

1. State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, China

Funder

Beijing Institute of Technology

State Key Laboratory of Explosion Science and Technology

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.4c00587

Reference71 articles.

1. Computational predictions of energy materials using density functional theory

2. An atlas of two-dimensional materials

3. Molecular dynamics simulations: advances and applications

4. The Role and Perspective of Ab Initio Molecular Dynamics in the Study of Biological Systems

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