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
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
physics.chem-ph 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
-
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.