1–4 Quantum chemistry methods, such as density functional theory, are obvious approaches to simulate such systems however, they are computationally very expensive for large systems. Introduction Large scale atomistic simulation of multi-component systems is a difficult task but they are highly valuable for industrial applications such as designing alloys, designing electrical contacts, touch screens, transistors, batteries, composites and catalysts. 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. 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 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. MLFFs have hitherto largely been designed and fitted for specific systems and are not usually transferable to chemistries beyond the specific training set. Classical force fields (FFs) based on machine learning (ML) methods show great potential for large scale simulations of solids.
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