Corpus ID: 237635369

Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties

  title={Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties},
  author={Jigyasa Nigam and Michael J. Willatt and Michele Ceriotti},
Symmetry considerations are at the core of the major frameworks used to provide an effective mathematical representation of atomic configurations, that are then used in machine-learning models to predict the properties associated with each structure. In most cases, the models rely on a description of atom-centered environments, and are suitable to learn atomic properties, or global observables that can be decomposed into atomic contributions. Many quantities that are relevant for quantum… Expand
3 Citations
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Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models
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Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems.
This work introduces a formalism that extends existing schemes and makes it possible to perform machine learning of tensorial properties of arbitrary rank, and for general molecular geometries, and derives a tensor kernel adapted to rotational symmetry. Expand
Molecular symmetry and spectroscopy
Editorial Board: W. G. E. Caldwell, OC, FRSC (University of Western Ontario); K. G. Davey, OC, FRSC (York University); S. Gubins (Annual Reviews); B. K. Hall, FRSC (Dalhousie University); P.Expand
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