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- W3176069906 abstract "Abstract In recent years, we have witnessed a widespread application of machine learning techniques in the field of materials science, owing to the increased availability of research data and sophisticated algorithms. At the core of this technology lies the ability to encode material structures into descriptors that are understandable for a computer. Although significant advances have been made in this area, there is a continued need to explore efficient structure‐encoding strategies so as to maximize the predictive power of the machine learning models. Here we present a revision of the exciting progress in four representative structural features that are capable of describing the structures of diverse materials: structure graph, Coulomb matrix, topological descriptor, and diffraction fingerprint. Particular attention is given to the studies of crystalline solids, which appear more challenging to be encoded than molecules. By summarizing previous works and presenting critical appraisals of these descriptors, this review could offer some guideline for the selection of structural features and stimulate inspiration for the design of powerful descriptors suited towards different tasks. This article is categorized under: Structure and Mechanism > Computational Materials Science Data Science > Artificial Intelligence/Machine Learning" @default.
- W3176069906 created "2021-07-05" @default.
- W3176069906 creator A5021329144 @default.
- W3176069906 creator A5032780084 @default.
- W3176069906 creator A5034454937 @default.
- W3176069906 creator A5038600944 @default.
- W3176069906 creator A5054656207 @default.
- W3176069906 creator A5055477551 @default.
- W3176069906 date "2021-06-18" @default.
- W3176069906 modified "2023-10-15" @default.
- W3176069906 title "Encoding the atomic structure for machine learning in materials science" @default.
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