Review: Machine learning descriptors to unify molecular and bulk systems

To use machine learning techniques to understand (and predict) structure-property relationships within chemistry, it is first necessary to represent the chemical structure in a computer-readable way. In this perspective review, we outline some of the state-of-the-art methods used to represent structure with applications both for molecular and extended solid systems. We also suggest future areas of focus in the field in order to bridge the divide between machine learning in small molecules and crystalline solids.

Graph representations of small molecule and extended solid

K. Rossi and J. Cumby, Representations and descriptors unifying the study of molecular and bulk systems, International Journal of Quantum Chemistry, 120, 2020, e26151.