Efficient first principles based modeling via machine learning: from simple representations to high entropy materials

Published

April 17, 2024

Author(s)

Kangming Li, Kamal Choudhary, Brian DeCost, Michael Greenwood, Jason Hattrick-Simpers

Abstract

High-entropy materials (HEMs) have recently emerged as a significant category of materials, of- fering highly tunable properties. However, the scarcity of HEM data in existing density functional theory (DFT) databases, primarily due to computational expense, hinders the development of effec- tive modeling strategies for computational materials discovery. In this study, we introduce an open DFT dataset of alloys and employ machine learning (ML) methods to investigate the material rep- resentations needed for HEM modeling. Utilizing high-throughput DFT calculations, we generate a comprehensive dataset of 84k structures, encompassing both ordered and disordered alloys across a spectrum of up to seven components and the entire compositional range. We apply descriptor-based models and graph neural networks to assess how material information is captured across diverse chemical-structural representations. We first evaluate the in-distribution performance of ML mod- els to confirm their predictive accuracy. Subsequently, we explore the capability of ML models to generalize between ordered and disordered structures, between low-order and high-order alloys, and between equimolar and non-equimolar compositions. Our findings suggest that ML models can effectively generalize from cost-effective calculations of simpler systems to more complex scenarios. Additionally, we discuss the influence of dataset size and analyze the information loss associated with the use of unrelaxed structures. Overall, this research sheds light on several critical aspects of HEM modeling and offers insights for data-driven atomistic modeling of HEMs.

Citation

Journal of Materials Chemistry A

Pub Type

Journals

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https://doi.org/10.1039/D4TA00982G

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Citation

LI, K. , Choudhary, K. , DeCost, B. , Greenwood, M. and Hattrick-Simpers, J. (2024), Efficient first principles based modeling via machine learning: from simple representations to high entropy materials, Journal of Materials Chemistry A, [online], https://doi.org/10.1039/D4TA00982G, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957574 (Accessed November 21, 2024)