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An Entropy-Maximization Approach to Automated Training Set Generation for Interatomic Potentials

Journal Article · · Journal of Chemical Physics
DOI:https://doi.org/10.1063/5.0013059· OSTI ID:1813845
 [1];  [2]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Clemson Univ., SC (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
+ Show Author Affiliations

Machine learning-based interatomic potentials are currently garnering a lot of attention as they strive to achieve the accuracy of electronic structure methods at the computational cost of empirical potentials. Given their generic functional forms, the transferability of these potentials is highly dependent on the quality of the training set, the generation of which can be highly labor-intensive. Good training sets should at once contain a very diverse set of configurations while avoiding redundancies that incur cost without providing benefits. We formalize these requirements in a local entropy-maximization framework and propose an automated sampling scheme to sample from this objective function. We show that this approach generates much more diverse training sets than unbiased sampling and is competitive with hand-crafted training sets.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
89233218CNA000001
OSTI ID:
1813845
Alternate ID(s):
OSTI ID: 1657629
Report Number(s):
LA-UR--20-21236
Journal Information:
Journal of Chemical Physics, Journal Name: Journal of Chemical Physics Journal Issue: 9 Vol. 153; ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)Copyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

74 ATOMIC AND MOLECULAR PHYSICS
97 MATHEMATICS AND COMPUTING
Entropy
Force field
Interatomic potentials
Machine learning
Molecular dynamics