A snapshot review on soft-materials assembly design utilizing machine learning methods

TL;DR

This paper reviews recent advances in applying machine learning to soft-materials assembly design, highlighting new methods, software tools, and their integration with traditional simulation techniques.

Contribution

It provides a comprehensive overview of machine learning techniques in soft materials assembly, including design, characterization, and software development, filling a gap in current literature.

Findings

Machine learning enhances high-throughput crystal structure characterization.

Inverse design methods for building blocks are emerging.

Software tools are increasingly compatible with molecular dynamics engines.

Abstract

Since the surge of data in materials science research and the advancement in machine learning methods, an increasing number of researchers are introducing machine learning techniques into the next generation of materials discovery, ranging from neural-network learned potentials to automated characterization techniques for experimental images. In this snapshot review, we first summarize the landscape of techniques for soft materials assembly design that do not employ machine learning or artificial intelligence and then discuss specific machine-learning and artificial-intelligence-based methods that enhance the design pipeline, such as high-throughput crystal-structure characterization and the inverse design of building blocks for materials assembly and properties. Additionally, we survey the landscape of current developments of scientific software, especially in the context of their compatibility with traditional molecular dynamics engines such as LAMMPS and HOOMD-blue.