Journal
PHYSICAL CHEMISTRY CHEMICAL PHYSICS
Volume 19, Issue 20, Pages 12585-12603Publisher
ROYAL SOC CHEMISTRY
DOI: 10.1039/c7cp01108c
Keywords
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Funding
- EPSRC [EP/I001352/1]
- Gates Cambridge Trust
- ERC
- EPSRC [EP/I001352/1, EP/N035003/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/I001352/1, EP/N035003/1] Funding Source: researchfish
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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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