title: Energy landscapes for machine learning.
abstract: Machine learning techniques are being increasingly used as flexible non-linear fitting and prediction tools in the physical sciences. Fitting functions that exhibit multiple solutions as local minima can be analysed in terms of the corresponding machine learning landscape. Methods to explore and visualise molecular potential energy landscapes can be applied to these machine learning landscapes to gain new insight into the solution space involved in training and the nature of the corresponding predictions. In particular, we can define quantities analogous to molecular structure, thermodynamics, and kinetics, and relate these emergent properties to the structure of the underlying landscape. This Perspective aims to describe these analogies with examples from recent applications, and suggest avenues for new interdisciplinary research.
authors.affiliation: University Chemical Laboratories, Lensfield Road, Cambridge CB2 1EW, UK. dw34@cam.ac.uk.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Ballard
authors.affiliation: University Chemical Laboratories, Lensfield Road, Cambridge CB2 1EW, UK. dw34@cam.ac.uk.
authors.forename: Ritankar
authors.initials: R
authors.lastname: Das
authors.affiliation: University Chemical Laboratories, Lensfield Road, Cambridge CB2 1EW, UK. dw34@cam.ac.uk.
authors.forename: Stefano
authors.identifier: http://orcid.org/0000-0003-2028-2175
authors.initials: S
authors.lastname: Martiniani
authors.affiliation: Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, IN, USA.
authors.forename: Dhagash
authors.initials: D
authors.lastname: Mehta
authors.affiliation: Mathematics Department, Courant Institute, New York University, NY, USA.
authors.forename: Levent
authors.identifier: http://orcid.org/0000-0001-5403-4124
authors.initials: L
authors.lastname: Sagun
authors.affiliation: Microsoft Research Ltd, 21 Station Road, Cambridge, CB1 2FB, UK.
authors.forename: Jacob D
authors.initials: JD
authors.lastname: Stevenson
authors.affiliation: University Chemical Laboratories, Lensfield Road, Cambridge CB2 1EW, UK. dw34@cam.ac.uk.
authors.forename: David J
authors.identifier: http://orcid.org/0000-0002-3555-6645
authors.initials: DJ
authors.lastname: Wales