A transfer compression technique using semi-empirical data reduces molecular representation dimensions by a median 72% (range 36-98%) while retaining accuracy for energy, heat capacity, dipole moment and polarizability on QM9 and VQM24, and improves data efficiency for dipoles to 19% of training dat
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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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Property-Specific Molecular Representations via Feature-Space Transfer Compression
A transfer compression technique using semi-empirical data reduces molecular representation dimensions by a median 72% (range 36-98%) while retaining accuracy for energy, heat capacity, dipole moment and polarizability on QM9 and VQM24, and improves data efficiency for dipoles to 19% of training dat
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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.