Scientific intuition inspired by machine learning generated hypotheses

• URL: http://arxiv.org/abs/2010.14236v2
• Date: Mon, 14 Dec 2020 18:31:14 GMT
• Title: Scientific intuition inspired by machine learning generated hypotheses
• Authors: Pascal Friederich, Mario Krenn, Isaac Tamblyn, Alan Aspuru-Guzik
• Abstract summary: We shift the focus on the insights and the knowledge obtained by the machine learning models themselves. We apply gradient boosting in decision trees to extract human interpretable insights from big data sets from chemistry and physics. The ability to go beyond numerics opens the door to use machine learning to accelerate the discovery of conceptual understanding.
• Score: 2.294014185517203
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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