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PUBLICATIONS

Unified graph neural network force-field for the periodic table: solid state applications

Published

Author(s)

Kamal Choudhary, Brian DeCost, Lily Major, Keith Butler, Jeyan Thiyagalingam, Francesca Tavazza

Abstract

Classical force fields (FFs) based on machine learning (ML) methods show great potential for large scale simulations of solids. MLFFs have hitherto largely been designed and fitted for specific systems and are not usually transferable to chemistries beyond the specific training set. We develop a unified atomisitic line graph neural network-based FF (ALIGNN-FF) that can model both structurally and chemically diverse solids with any combination of 89 elements from the periodic table. To train the ALIGNN-FF model, we use the JARVIS-DFT dataset which contains around 75,000 materials and 4 million energy-force entries, out of which 307,113 are used in the training. We demonstrate the applicability of this method for fast optimization of atomic structures in the crystallography open database and by predicting accurate crystal structures using a genetic algorithm for alloys.
Citation
Digital Discovery
Pub Type
Journals

Download Paper

https://doi.org/10.1039/D2DD00096B
Local Download
Modeling and computational material science and Materials

Citation

Choudhary, K. , DeCost, B. , Major, L. , Butler, K. , Thiyagalingam, J. and Tavazza, F. (2023), Unified graph neural network force-field for the periodic table: solid state applications, Digital Discovery, [online], https://doi.org/10.1039/D2DD00096B, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935367 (Accessed October 8, 2025)

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Created February 23, 2023, Updated March 9, 2023
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