An automated approach for developing neural network interatomic potentials with FLAME

Mirhosseini H, Tahmasbi H, Kuchana SR, Ghasemi SA, Kühne TD (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Computational Materials Science Elsevier

Book Volume: 197

Article Number: 110567

DOI: 10.1016/j.commatsci.2021.110567

Abstract

The performance of machine learning interatomic potentials relies on the quality of the training dataset. In this work, we present an approach for generating diverse and representative training data points which initiates with ab initio calculations for bulk structures. The data generation and potential construction further proceed side-by-side in a cyclic process of training the neural network and crystal structure prediction based on the developed interatomic potentials. All steps of the data generation and potential development are performed with minimal human intervention. We show the reliability of our approach by assessing the performance of neural network potentials developed for two inorganic systems.

Involved external institutions

Universität Paderborn DE Germany (DE) Leiden University NL Netherlands (NL)

How to cite

APA:

Mirhosseini, H., Tahmasbi, H., Kuchana, S.R., Ghasemi, S.A., & Kühne, T.D. (2021). An automated approach for developing neural network interatomic potentials with FLAME. Computational Materials Science, 197. https://doi.org/10.1016/j.commatsci.2021.110567

MLA:

Mirhosseini, Hossein, et al. "An automated approach for developing neural network interatomic potentials with FLAME." Computational Materials Science 197 (2021).

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