Scientific intuition inspired by machine learning generated hypotheses

by   Pascal Friederich, et al.

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.


page 1

page 2

page 3

page 4


New Trends in Quantum Machine Learning

Here we will give a perspective on new possible interplays between Machi...

Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data

Machine learning models are a powerful theoretical tool for analyzing da...

Pedagogical Rule Extraction for Learning Interpretable Models

Machine-learning models are ubiquitous. In some domains, for instance, i...

Computer-inspired Quantum Experiments

The design of new devices and experiments in science and engineering has...

Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena

Interpretable machine learning (IML) is concerned with the behavior and ...

New Metric Formulas that Include Measurement Errors in Machine Learning for Natural Sciences

The application of machine learning to physics problems is widely found ...

Unsupervised machine learning for physical concepts

In recent years, machine learning methods have been used to assist scien...

Please sign up or login with your details

Forgot password? Click here to reset