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Scientific intuition inspired by machine learning-generated hypotheses
Machine Learning: Science and Technology ( IF 6.013 ) Pub Date : 2021-04-15 , DOI: 10.1088/2632-2153/abda08
Pascal Friederich 1, 2, 3, 4 , Mario Krenn 1, 2, 5 , Isaac Tamblyn 5, 6 , Aln Aspuru-Guzik 1, 2, 5, 7
Affiliation  

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.



中文翻译:

受机器学习产生的假设启发的科学直觉

应用于物理科学问题的机器学习已成为一种广泛使用的工具,成功应用于许多领域的分类、回归和优化任务。研究重点主要在于提高机器学习模型在数值预测中的准确性,而科学理解仍然几乎完全由人类研究人员分析数值结果并得出结论。在这项工作中,我们将重点转移到机器学习模型本身获得的见解和知识上。特别是,我们研究如何提取和使用它来激励人类科学家增加他们对自然系统的直觉和理解。我们在决策树中应用梯度提升,从化学和物理的大数据集中提取人类可解释的见解。在化学中,我们不仅重新发现广为人知的经验法则,而且还发现了新的有趣主题,这些主题告诉我们如何控制有机分子的溶解度和能级。同时,在量子物理学中,我们对量子纠缠实验有了新的认识。超越数字并进入科学洞察力和假设生成领域的能力为使用机器学习加速发现一些最具挑战性的科学领域的概念理解打开了大门。

更新日期:2021-04-15
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