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Classical force fields (FFs) remain the workhorse for large-scale simulations even as machine-learned interatomic potentials (MLIPs) approach ab initio accuracy. They decompose total configuration energies into simple effective interactions whose parameters are traditionally assigned based on atom or bond types, enabli...
This work constructs an invariant representation of the reciprocal lattice that is independent of primitive cell convention, based on the bispectrum--a descriptor built from spherical harmonic projections of lattice points that could serve more broadly as a geometry-aware target for other crystallographic machine learn...
This work investigates the inverse problem of recovering atomic structures from local invariant descriptors, and shows that accurate reconstructions can be obtained from remarkably compact descriptors of different correlation orders, each comprising only a few tens of features.
This work proposes an operator-centric framework in which the external (nuclear) potential, expressed in an AO basis, serves as the model input and builds hierarchical, body-ordered representations of atomic configurations that closely mirror the principles underlying several popular atom-centered descriptors.
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