NequIP
PulseAugur coverage of NequIP — every cluster mentioning NequIP across labs, papers, and developer communities, ranked by signal.
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New optimizers outperform Adam for faster MLIP training · 3 sources tracked
A new research paper explores the impact of optimizers on the training of machine learning interatomic potentials (MLIPs), a key AI application in scientific simulation. The study found that matrix-structured optimizers…
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New methods boost accuracy of interatomic potential models
Researchers have developed novel methods, Physics-Aware Neighborhood (PAN) pooling and Physics-Guided Spectral (PGS) mixers, to enhance the accuracy of short-range equivariant interatomic potentials. These techniques fo…
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New MLIP methods improve accuracy and automate research
Researchers are developing advanced machine learning interatomic potentials (MLIPs) to improve atomistic simulations. New methods like Stein Kernelized Molecular Dynamics (SKMD) enhance data acquisition for active learn…