Soft-FQEq makes fragment charge equilibration differentiable via geometry-dependent soft connectivity in reactive MLIPs, recovering sustained electrochemical potential gradients at interfaces that global QEq collapses.
Finkler, Stefan Goedecker, and Jörg Behler
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Introduces torch-pme and jax-pme libraries that embed Ewald-based long-range methods and purified descriptors into atomistic ML for accurate handling of non-local physical interactions.
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Fragment-Constrained Charge Equilibration for Charge-Aware Machine Learning Potentials at Electrochemical Interfaces
Soft-FQEq makes fragment charge equilibration differentiable via geometry-dependent soft connectivity in reactive MLIPs, recovering sustained electrochemical potential gradients at interfaces that global QEq collapses.
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Fast and flexible long-range models for atomistic machine learning
Introduces torch-pme and jax-pme libraries that embed Ewald-based long-range methods and purified descriptors into atomistic ML for accurate handling of non-local physical interactions.