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molecular simulation techniques (density functional theory, molecular dynamics, ab initio molecular dynamics) and in developing machine learning interatomic potentials and can apply these to uncover atomistic
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and machine-learning potentials for planetary materials. Curating and generating large-scale ab initio datasets across wide pressureâ“temperature regimes. Designing and training advanced machine
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systems at various scales, for example using ab initio electronic structure methods like density-functional theory, developing interatomic potentials with various methodologies including machine learning
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expertise in methods such as machine-learning force-fields for spinful materials, or multi-fidelity Bayesian models that can learn machine-learning force-fields along with effective spin Hamiltonians from ab
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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte