Generative Models for Crystalline Materials

TL;DR

This paper reviews the current state of generative machine learning models for predicting and creating crystal structures in materials science, highlighting methods, challenges, and future directions.

Contribution

It provides a comprehensive overview of generative models for crystal structure prediction, including representations, evaluation, and emerging topics in the field.

Findings

Analysis of various crystal representations and generative models.

Evaluation of strengths and limitations of current approaches.

Discussion of experimental considerations and future challenges.

Abstract

Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has emerged as a powerful tool for advancing this understanding and accelerating materials discovery. Early ML approaches primarily focused on constructing and screening large material spaces to identify promising candidates for various applications. More recently, research efforts have increasingly shifted toward generating crystal structures using end-to-end generative models. This review analyzes the current state of generative modeling for crystal structure prediction and de novo generation. It examines crystal representations, outlines the generative models used to design crystal structures, and evaluates their respective strengths and limitations. Furthermore, the review highlights experimental considerations for evaluating generated structures and provides recommendations for suitable existing software tools. Emerging topics, such as modeling disorder and defects, integration in advanced characterization, incorporating synthetic feasibility constraints, and model explainability are explored. Ultimately, this work aims to inform both experimental scientists looking to adapt suitable ML models to their specific circumstances and ML specialists seeking to understand the unique challenges related to inverse materials design and discovery.