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.
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.
citing papers explorer
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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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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.