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 language | English |
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Article number | e26151 |
Journal | International journal of quantum chemistry |
Early online date | 27 Dec 2019 |
DOIs | |
Publication status | E-pub ahead of print - 27 Dec 2019 |
Keywords / Materials (for Non-textual outputs)
- atom density
- connectivity
- data driven
- descriptors
- machine learning
- symmetry distortions