Training data selection for accuracy and transferability of interatomic potentials.

Autor: Montes de Oca Zapiain, David, Wood, Mitchell A., Lubbers, Nicholas, Pereyra, Carlos Z., Thompson, Aidan P., Perez, Danny
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Zdroj: NPJ Computational Materials; 9/1/2022, Vol. 8 Issue 1, p1-9, 9p
Abstrakt: Advances in machine learning (ML) have enabled the development of interatomic potentials that promise the accuracy of first principles methods and the low-cost, parallel efficiency of empirical potentials. However, ML-based potentials struggle to achieve transferability, i.e., provide consistent accuracy across configurations that differ from those used during training. In order to realize the promise of ML-based potentials, systematic and scalable approaches to generate diverse training sets need to be developed. This work creates a diverse training set for tungsten in an automated manner using an entropy optimization approach. Subsequently, multiple polynomial and neural network potentials are trained on the entropy-optimized dataset. A corresponding set of potentials are trained on an expert-curated dataset for tungsten for comparison. The models trained to the entropy-optimized data exhibited superior transferability compared to the expert-curated models. Furthermore, the models trained to the expert-curated set exhibited a significant decrease in performance when evaluated on out-of-sample configurations. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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