Machine Learning and Machine Discovery: Lessons and Challenges of Data-Centric Natural Sciences

Abstract

  Machine learning is increasingly becoming a daily tool for natural scientists, forging collaborations across disciplines. This article presents a brief overview from a modern machine-learning viewpoint on what machine learning is, how it can be useful for natural science research, and how it can transform our way of doing science. Every natural science field is now facing diverse experimental, simulated, and literature-based data, and trying to leverage this accessibility and multiplicity of heterogenous views to full advantage. Lessons from data-centric multidisciplinary research over recent years well as common pitfalls and rising challenges are discussed.