SKMD adapts Stein variational gradient descent into molecular dynamics with asynchronous updates and global atomic descriptor kernels to acquire non-redundant training configurations while preserving the Boltzmann distribution, yielding higher MLIP accuracy with fewer samples than baselines.
Computer simulation of local order in condensed phases of silicon
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GPU-PF and PRISMS-PF phase-field codes produce consistent predictions for dendritic morphology, primary spacing, and tip dynamics in 2D and 3D simulations of alloy solidification at experimentally relevant scales.
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Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials
SKMD adapts Stein variational gradient descent into molecular dynamics with asynchronous updates and global atomic descriptor kernels to acquire non-redundant training configurations while preserving the Boltzmann distribution, yielding higher MLIP accuracy with fewer samples than baselines.
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Benchmarking of Massively Parallel Phase-Field Codes for Directional Solidification
GPU-PF and PRISMS-PF phase-field codes produce consistent predictions for dendritic morphology, primary spacing, and tip dynamics in 2D and 3D simulations of alloy solidification at experimentally relevant scales.