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arXiv · 1703.07915

Perspective: Energy Landscapes for Machine Learning

Andrew J. Ballard·Ritankar Das·Stefano Martiniani·Dhagash Mehta·Levent Sagun·Jacob D. Stevenson·David J. Wales
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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.

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Andrew J. Ballard, Ritankar Das, Stefano Martiniani, Dhagash Mehta, Levent Sagun, Jacob D. Stevenson, David J. Wales. 2017-03-23. Perspective: Energy Landscapes for Machine Learning. https://doi.org/10.1039/c7cp01108c

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