Random projections and Kernelised Leave One Cluster Out Cross-Validation: Universal baselines and evaluation tools for supervised machine learning for materials properties

TL;DR

This paper evaluates how kernelized leave-one-cluster-out cross-validation improves the assessment of machine learning models in materials science, providing a universal framework that enhances the evaluation process across various representations and algorithms.

Contribution

It introduces a kernelized LOCO-CV framework that improves the evaluation of machine learning models for materials properties, regardless of the representation or algorithm used.

Findings

Radial basis function improves data separability in all tested datasets.

Kernel approximation functions enhance LOCO-CV performance.

Domain knowledge does not significantly improve prediction accuracy in most cases.

Abstract

With machine learning being a popular topic in current computational materials science literature, creating representations for compounds has become common place. These representations are rarely compared, as evaluating their performance - and the performance of the algorithms that they are used with - is non-trivial. With many materials datasets containing bias and skew caused by the research process, leave one cluster out cross validation (LOCO-CV) has been introduced as a way of measuring the performance of an algorithm in predicting previously unseen groups of materials. This raises the question of the impact, and control, of the range of cluster sizes on the LOCO-CV measurement outcomes. We present a thorough comparison between composition-based representations, and investigate how kernel approximation functions can be used to better separate data to enhance LOCO-CV applications. We find that domain knowledge does not improve machine learning performance in most tasks tested, with band gap prediction being the notable exception. We also find that the radial basis function improves the linear separability of chemical datasets in all 10 datasets tested and provide a framework for the application of this function in the LOCO-CV process to improve the outcome of LOCO-CV measurements regardless of machine learning algorithm, choice of metric, and choice of compound representation. We recommend kernelised LOCO-CV as a training paradigm for those looking to measure the extrapolatory power of an algorithm on materials data.