Insights into machine learning models from chemical physics: an energy landscapes approach (EL for ML)

Acknowledgements: DJW gratefully acknowledges an International Chair at the Interdisciplinary Institute for Artificial Intelligence at 3iA Cote d'Azur, supported by the French government, with reference number ANR-19-P3IA-0002, which has provided interactions that furthered the present research project. MPN acknowledges funding from Downing College, Cambridge. LD and EOP-K would like to acknowledge this work was supported by the Hartree National Centre for Digital Innovation – a collaboration between Science and Technology Facilities Council and IBM. LD also acknowledges the financial support of the EPSRC via a knowledge transfer fellowship.In this work, we outline how methods from the energy landscapes field of theoretical chemistry can be applied to study machine learning models. Various applications are found, ranging from interpretability to improved model performance.</jats:p