Generative Models for Crystalline Materials
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917304652005376 |
|---|---|
| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22652 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative Models for Crystalline Materials 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 Materials Science Machine Learning 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. |
| title | Generative Models for Crystalline Materials |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2511.22652 |