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Topics
Quantum Machine Learning
Quantum Chemistry
Representations and descriptors unifying the study of molecular and bulk systems
Crossref
Authors: Kevin Rossi, James Cumby
Year
2019
Paper ID
5433
Status
Peer-reviewed
Abstract Read
~2 min
Abstract Words
81
Citations
N/A
Abstract
AbstractEstablishing 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.
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- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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- AbstractEstablishing a unified framework for describing the structures of molecular and periodic systems is a long‐standing challenge in physics, chemistry, and material science.
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