Representations and descriptors unifying the study of molecular and bulk systems

Kevin Rossi, James Cumby*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article numbere26151
JournalInternational journal of quantum chemistry
Early online date27 Dec 2019
DOIs
Publication statusE-pub ahead of print - 27 Dec 2019

Keywords

  • atom density
  • connectivity
  • data driven
  • descriptors
  • machine learning
  • symmetry distortions

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