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Generative Models for Crystalline Materials

Title: Generative Models for Crystalline Materials
Authors: Metni, Houssam; Ruple, Laura; Walters, Lauren N.; Torresi, Luca; Teufel, Jonas; Schopmans, Henrik; Östreicher, Jona; Zhang, Yumeng; Neubert, Marlen; Koide, Yuri; Steiner, Kevin; Link, Paul; Bär, Lukas; Petrova, Mariana; Ceder, Gerbrand; Friederich, Pascal
Source: Advanced Materials, 38 (18) ; ISSN: 0935-9648, 1521-4095
Publisher Information: John Wiley and Sons
Publication Year: 2026
Collection: KITopen (Karlsruhe Institute of Technologie)
Subject Terms: crystalline materials; generative models; inverse materials design; machine learning; ddc:600; Technology; info:eu-repo/classification/ddc/600
Description: 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.
Document Type: article in journal/newspaper
File Description: application/pdf
Language: English
ISSN: 0935-9648; 1521-4095
Relation: info:eu-repo/semantics/altIdentifier/wos/001700861200001; info:eu-repo/semantics/altIdentifier/issn/0935-9648; info:eu-repo/semantics/altIdentifier/issn/1521-4095; https://publikationen.bibliothek.kit.edu/1000191566; https://publikationen.bibliothek.kit.edu/1000191566/177307467; https://doi.org/10.5445/IR/1000191566
DOI: 10.5445/IR/1000191566
Availability: https://publikationen.bibliothek.kit.edu/1000191566; https://publikationen.bibliothek.kit.edu/1000191566/177307467; https://doi.org/10.5445/IR/1000191566
Rights: https://creativecommons.org/licenses/by/4.0/deed.de ; info:eu-repo/semantics/openAccess
Accession Number: edsbas.41736EC8
Database: BASE