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- W3185227028 endingPage "9815" @default.
- W3185227028 startingPage "9759" @default.
- W3185227028 abstract "The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable representation. The development of atomic-scale representations has played, and continues to play, a central role in the success of machine-learning methods for chemistry and materials science. This review summarizes the current understanding of the nature and characteristics of the most commonly used structural and chemical descriptions of atomistic structures, highlighting the deep underlying connections between different frameworks and the ideas that lead to computationally efficient and universally applicable models. It emphasizes the link between properties, structures, their physical chemistry, and their mathematical description, provides examples of recent applications to a diverse set of chemical and materials science problems, and outlines the open questions and the most promising research directions in the field." @default.
- W3185227028 created "2021-08-02" @default.
- W3185227028 creator A5021241296 @default.
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- W3185227028 creator A5049876834 @default.
- W3185227028 creator A5061841111 @default.
- W3185227028 creator A5071518647 @default.
- W3185227028 creator A5079309376 @default.
- W3185227028 date "2021-07-26" @default.
- W3185227028 modified "2023-10-17" @default.
- W3185227028 title "Physics-Inspired Structural Representations for Molecules and Materials" @default.
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