Work

Representations and descriptors unifying the study of molecular and bulk systems

Year: 2019

Type: article

Abstract: Establishing a unified framework for describing the structures of molecular and periodic systems is a long‐standing challenge in physics, chemistry, and material science. With the rise of machine learning methods in these fields, there is a growing need for such a method. This perspective aims to discuss the development and use of three promising approaches—topological, atom‐density, and symmetry‐based—for the prediction and rationalization of physical, chemical, and mechanical properties of atomistic systems across different scales and compositions.

Source: International Journal of Quantum Chemistry

DOI: 10.1002/qua.26151

Authors: Kevin Rossi, James Cumby

Institutions: École Polytechnique Fédérale de Lausanne, University of Edinburgh

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: bronze

Funder: École Polytechnique Fédérale de Lausanne

OpenAlex ID: W2997295157

FWCI: 1.215

Cites: 53

Cited by: 18

Related to: 10