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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
Presentation Title NOW ON-DEMAND ONLY - Ultra-fast and Interpretable Machine-learning Potentials with Application to Structure Prediction
Author(s) Stephen Raymond Xie, Matthias Rupp, Richard G. Hennig
On-Site Speaker (Planned) Richard G. Hennig
Abstract Scope Although ab initio methods are indispensable tools for predicting properties of materials and simulating chemical processes, the tradeoff between computational efficiency and predictive accuracy limits their application to large systems and long simulation times. To address this challenge, we combine effective two- and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are as fast as traditional empirical potentials, sufficiently accurate for applications, and physically interpretable. We benchmark using a bcc tungsten dataset, including melting point calculations with thousands of atoms. Finally, we discuss applying the introduced potentials in accelerating structure prediction. By coupling the Genetic Algorithm for Structure Prediction (GASP) to our machine-learning approach, we train a potential on-the-fly using configurations from the structure search. As the potential learns the energy landscape, we use the potential as a surrogate model to filter candidate structures, reducing the number of ab initio calculations required.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation

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