Year: 2024
Type: article
Abstract: High-dimensional representations of the elements have become common within the field of materials informatics to build useful, structure-agnostic models for the chemistry of materials. However, the characteristics of elements change when they adopt a given oxidation state, with distinct structural preferences and physical properties. We explore several methods for developing embedding vectors of elements decorated with oxidation states. Graphs generated from 110,160 crystals are used to train representations of 84 elements that form 336 species. Clustering these learned representations of ionic species in low-dimensional space reproduces expected chemical heuristics, in particular the separation of cations from anions. We show that these representations have enhanced expressive power for property prediction tasks involving inorganic compounds. We expect that ionic representations, necessary for the description of mixed valence and complex magnetic systems, will support more powerful machine learning models for materials.
Source: APL Machine Learning
DOI: 10.1063/5.0227009
Authors: Anthony Onwuli, Keith T. Butler, Aron Walsh
Institutions: Imperial College London, University College London
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4402643573