Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements.
By M. J. Willatt, Félix Musil, Michele Ceriotti. 2018
59 citations recorded by the source corpus.
Subfields: Geometry and Topology, Materials Chemistry, Physical and Theoretical Chemistry
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In Materials Chemistry
- Ilyes Batatia
- Christoph Ortner
- D. Kov'acs
- Gábor Csányi
- Gregor N. C. Simm
- Alexander V. Shapeev
- Evgeny V. Podryabinkin
- James R. Kermode
In Physical and Theoretical Chemistry
Citations in the corpus
- MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields (2022, methodology): “Data-driven compression schemes have been proposed [50] to solve this issue, and MPNNs incorporate similar embeddings of the chemical elements into a fixed-size vector space.”