Ballard Andrew J, Das Ritankar, Martiniani Stefano, Mehta Dhagash, Sagun Levent, Stevenson Jacob D, Wales David J
University Chemical Laboratories, Lensfield Road, Cambridge CB2 1EW, UK.
Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, IN, USA.
Phys Chem Chem Phys. 2017 May 24;19(20):12585-12603. doi: 10.1039/c7cp01108c.
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.
机器学习技术在物理科学中越来越多地被用作灵活的非线性拟合和预测工具。对于那些具有多个局部极小值解的拟合函数，可以根据相应的机器学习态势进行分析。探索和可视化分子势能态势的方法可应用于这些机器学习态势，以便深入了解训练中涉及的解空间以及相应预测的本质。特别是，我们可以定义类似于分子结构、热力学和动力学的量，并将这些涌现特性与底层态势的结构联系起来。本视角旨在通过近期应用的实例描述这些类比，并提出新的跨学科研究途径。
