Recent advances in accelerated discovery through machine learning and statistical inference

TL;DR

This paper reviews how recent machine learning and statistical inference advances are accelerating discovery in physical chemistry by improving experimental design, simulations, modeling, and material discovery through synergistic experiment-computation integration.

Contribution

It highlights recent case studies demonstrating the integration of machine learning and statistical inference to enhance discovery processes in physical chemistry.

Findings

Automated experimental design improves efficiency.

Machine learning accelerates molecular simulations.

Predictive models aid in material discovery.

Abstract

Recent applications of machine learning and statistical inference provide case studies demonstrating how such approaches can accelerate the discovery process in physical chemistry and related fields. Examples discussed in this review include the introduction of automated approaches to systematically improve experimental design, increase the efficiency of computationally expensive molecular simulations, facilitate construction of predictive models for complex biological processes, and discover interparticle potentials that lead to materials which meet specified design goals. A common theme is the synergy between experiment and computation enabled by such approaches.