Unified theory of atom-centered representations and message-passing machine-learning schemes
Creators
- 1. Laboratory of Computational Science and Modelling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
- 2. National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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Description
Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on descriptions of atom-centered environments, that are associated with an atomic property or with an atomic contribution to an extensive macroscopic quantity. Frameworks in this class can be understood in terms of atom-centered density correlations (ACDC), that are used as a basis for a body-ordered, symmetry-adapted expansion of the targets. Several other schemes, that gather information on the relationship between neighboring atoms using "message-passing" ideas, cannot be directly mapped to correlations centered around a single atom. We generalize the ACDC framework to include multi-centered information, generating representations that provide a complete linear basis to regress symmetric functions of atomic coordinates, and provides a coherent foundation to systematize our understanding of both atom-centered and message-passing, invariant and equivariant machine-learning schemes. This record contains the data and code required to reproduce the results from the corresponding paper, computing message-passing inspired machine learning features built on top of density correlation. The data used in this article is a subset of other existing datasets, which can be found online: - methane dataset: https://archive.materialscloud.org/record/2020.105 - NaCl dataset: https://github.com/dilkins/TENSOAP/tree/ea671154b3642b4ec879a4292a4dd4399ddbdea6/example/random_nacl - QM7 and QM9 with dipole moments: https://archive.materialscloud.org/record/2020.56
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References
Journal reference (Paper where this data is used as examples for the message passing density correlation features) J. Nigam, S. Pozdnyakov, G. Fraux, M. Ceriotti J. Chem. Phys. 156, 204115 (2022), doi: 10.1063/5.0087042