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Meeting	MS&T22: Materials Science & Technology
Symposium	Uncertainty Quantification in Data-Driven Materials and Process Design
Presentation Title	Using Scalable Multi-Objective Bayesian Optimization to Develop Aluminum Scandium Nitride Molecular Dynamics Force Fields
Author(s)	Jesse M. Sestito, Michaela Kempner, Tequila A. L. Harris, Eva Zarkadoula, Yan Wang
On-Site Speaker (Planned)	Jesse M. Sestito
Abstract Scope	Scandium (Sc) doped aluminum nitride (AlScN) exhibits improved piezoelectric properties. To fine tune the material properties for design purposes, an atomistic level understanding of the structure-property (S-P) relationships is needed. Molecular dynamics can be used to understand the S-P relationships. However, the limited availability of force fields has been a challenge for property predictions. In this work, a force field calibration method using scalable multi-objective Bayesian optimization is presented. Optimizations with three, six, and eight objectives are applied to calibrate AlScN force fields based on piezoelectric characteristics, modulus of elasticity, and lattice parameters at different doped levels. The performances of the different force fields are compared, and the performance of the higher dimensional objective problems is discussed. The highly scalable molecular dynamics force field development method is successfully implemented, resulting in the creation of several aluminum scandium nitride molecular dynamics force fields for piezoelectric applications at varying Sc dope levels.

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Using Scalable Multi-Objective Bayesian Optimization to Develop Aluminum Scandium Nitride Molecular Dynamics Force Fields

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