Scientific intuition inspired by machine learning generated hypotheses

TL;DR

This paper demonstrates how machine learning models, specifically gradient boosting decision trees, can be used to extract interpretable scientific insights and generate hypotheses in chemistry and quantum physics, enhancing human understanding.

Contribution

It introduces a method to derive scientific insights directly from machine learning models, moving beyond numerical predictions to hypothesis generation in scientific research.

Findings

Rediscovered known rules of thumb in chemistry

Identified new motifs controlling solubility and energy levels

Gained new understanding of quantum entanglement experiments

Abstract

Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in many areas. Research focus mostly lies in improving the accuracy of the machine learning models in numerical predictions, while scientific understanding is still almost exclusively generated by human researchers analysing numerical results and drawing conclusions. In this work, we shift the focus on the insights and the knowledge obtained by the machine learning models themselves. In particular, we study how it can be extracted and used to inspire human scientists to increase their intuitions and understanding of natural systems. We apply gradient boosting in decision trees to extract human interpretable insights from big data sets from chemistry and physics. In chemistry, we not only rediscover widely know rules of thumb but also find new interesting motifs that tell us how to control solubility and energy levels of organic molecules. At the same time, in quantum physics, we gain new understanding on experiments for quantum entanglement. The ability to go beyond numerics and to enter the realm of scientific insight and hypothesis generation opens the door to use machine learning to accelerate the discovery of conceptual understanding in some of the most challenging domains of science.