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.
Cignoni , author D
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Machine learning models that respect material symmetries are accelerating the identification of topological phases and the discovery of d-wave, g-wave, and i-wave altermagnets in quantum materials.
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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.
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Machine Learning and Deep Learning in Quantum Materials: Symmetry, Topology, and the Rise of Altermagnets
Machine learning models that respect material symmetries are accelerating the identification of topological phases and the discovery of d-wave, g-wave, and i-wave altermagnets in quantum materials.