Conference Tools for 2024 TMS Annual Meeting & Exhibition

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About this Abstract
Meeting	2024 TMS Annual Meeting & Exhibition
Symposium	Materials Informatics to Accelerate Nuclear Materials Investigation
Presentation Title	Influence of Empirical Potentials on Data Quality in Computational Studies of Zr Alloys
Author(s)	Oliver Nicholls, Vidur Tuli, Patrick A. Burr
On-Site Speaker (Planned)	Patrick A. Burr
Abstract Scope	The quality of empirical potentials significantly influences the reliability of the data obtained from computational studies. In this study, we compare 13 popular Zr potentials for their ability to reproduce key physical, mechanical, structural and thermodynamic properties. These include thermal expansion, melting point, volume-energy response, allotropic phase stability, elastic properties, and point defect energies. No potential outperforms others in all aspects, highlighting the importance of selecting appropriate potentials. Older EAM potentials excel in a few metrics but lack transferability. Machine learning-trained potentials have lower overall accuracy and transferability compared to simpler available potentials. The prediction of point defect structures and energies exhibited the greatest divergence and least accuracy. To aid potential selection, maps are created based on the potentials' performance. This study emphasises the dependence of computational data quality on potential quality, underscoring the need for reliable empirical potentials to ensure trustworthy informatics.
Proceedings Inclusion?	Planned:
Keywords	Nuclear Materials, Modeling and Simulation, Computational Materials Science & Engineering

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