Publication date: Mar 24, 2022
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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File name | Size | Description |
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methane-40k.xyz
MD5md5:70d7f45c49b725a8f95a3e7c763a291c
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13.0 MiB | Extended XYZ file containing the methane structures and energies |
methane.zip
MD5md5:f8a03c12c20114ef88237f61c725560d
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6.5 KiB | Scripts used to compute message passing features and use them to train models for methane |
random-nacl.xyz
MD5md5:9e76b4a8972a2e829335163fe31c7d7c
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6.8 MiB | Extended XYZ file containing the random NaCl structures and energies |
nacl.zip
MD5md5:e03a06c9b6c4657590280d78797cc14d
|
7.5 KiB | Scripts used to compute message passing features and use them to train models for NaCl |
qm7_shuffled_chno.xyz
MD5md5:6b437f8c3ad640ac3676c8e43d98e965
|
8.3 MiB | Extended XYZ file containing the subset of QM7 used in this study |
qm7_chno_dipole.npy
MD5md5:a44c0931c24788625daa60ce1afea85b
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161.2 KiB | Dipole moments of QM7 structures in numpy format |
qm9_1000_test.xyz
MD5md5:97406dd69fd0ec945fcadf432a340051
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1.1 MiB | Extended XYZ file containing the subset of QM9 used in this study |
qm9_dipole.npy
MD5md5:c90dc2576831316f43df392bfcf91af7
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23.5 KiB | Dipole moments of QM9 structures in numpy format |
qm7_dipole.zip
MD5md5:2966a6260def1b073e2a3c90b6e354f1
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6.4 KiB | Scripts used to compute message passing features and use them to train models for QM7/QM9 |
2022.44 (version v1) [This version] | Mar 24, 2022 | DOI10.24435/materialscloud:3f-g3 |