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Found 4 papers, 1 papers with code
Phase discovery with active learning: Application to structural phase transitions in equiatomic NiTi
Here, we train four machine-learned force fields for equiatomic NiTi based on the LDA, PBE, PBEsol, and SCAN DFT functionals.
Learning Interatomic Potentials at Multiple Scales
When running MD, the MTS integrator then evaluates the smaller model for every time step and the larger model less frequently, accelerating simulation.
Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size
This work brings the leading accuracy, sample efficiency, and robustness of deep equivariant neural networks to the extreme computational scale.
Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
This work introduces Allegro, a strictly local equivariant deep learning interatomic potential that simultaneously exhibits excellent accuracy and scalability of parallel computation.
