Machine Learning in Soft Matter: From Simulations to Experiments

Soft matter with diverse functionalities that are easily designable has fascinated tremendous research interests in the past several decades. Nevertheless, the inherent confluence of time and length scale ubiquitous in soft matter immensely complicates the elucidation of the structure-property relationship and thereby severely impedes the function exploration of soft materials. Recently, the emergent machine learning (ML) techniques open new paradigms in property prediction and molecular design of functional materials, due to their extraordinarily distinguished performance in the aspect of trend identity and pattern extraction from data, and objective optimization by accelerating the guided search in high-dimensional spaces. This review exclusively focuses on the current state-of-the-art progress in the development of ML techniques applied in the realms of soft matter, ranging from coarse-grained simulations to theoretical prediction on the structural formation and macroscopic properties, as well as the optimization and algorithm-aided design in experiments. Finally, an outlook on the challenges and opportunities for this rapidly evolving field is discussed. Attractive characteristics of soft matter facilitate to fabricate various novel materials. However, intricate multiscale behaviors impede the function exploration of soft materials. This article summarizes the application of machine learning in soft matter from simulations to experiments, and proposes the possible challenges and prospects in the upcoming future, which would provide a guidance for this rapidly evolving field. image