# Perspective: Energy Landscapes for Machine Learning

## TL;DR

This paper explores the analogy between molecular energy landscapes and machine learning landscapes, providing insights into solution spaces and prediction behaviors through visualization and interdisciplinary approaches.

## Contribution

It introduces methods to analyze and visualize machine learning landscapes using concepts from molecular energy landscapes, fostering new interdisciplinary research.

## Key findings

- Machine learning landscapes can be analyzed using molecular energy landscape techniques.
- Visualization methods reveal the structure of solution spaces in training.
- Analogies to thermodynamics and kinetics provide new insights into model behavior.

## 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.