Crystal Structure Generation

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8 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 43

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Composable Crystals: Controllable Materials Discovery via Concept Learning
May 14, 2026Nian Liu, Yuwei Zeng, Ryoji Kubo +7Vector-Quantized Variational Autoencoder Crystal Structure Generation
National University of Singapore · Loyola Marymount University
Crys-JEPA: Accelerating Crystal Discovery via Embedding Screening and Generative Refinement
May 14, 2026Nian Liu, Nikita Kazeev, Stephen Gregory Dale +8Joint-Embedding Predictive Architecture Generative Modeling
1National University of Singapore · 2Loyola Marymount University · 3New York University +1
CrystalReasoner: Reasoning and RL for Property-Conditioned Crystal Structure Generation
May 14, 2026Yuyang Wu, Stefano Falletta, Delia McGrath +1Crystal Structure Generation RL for Generative Models
Tsinghua University Beijing, China · Radical AI · New York University New York, NY, USA
Generating Symmetric Materials using Latent Flow Matching
May 11, 2026Anmar Karmush, Cedric Mathieu Brandenburg, Soheil Ershadrad +3Crystal Structure Generation Computational Materials Science
Department of Electrical Engineering (ISY) & AI4x, Linköping University · Department of Physics, Chemistry and Biology (IFM), Linköping University · Wallenberg Initiative Materials Science for Sustainability (WISE), Linköping University +1
CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models
May 9, 2026Chengqian Zhang, Yucheng Jin, Duo Zhang +2Transfer Learning Crystal Structure Generation
AI for Science Institute, Beijing, China · Center for Data Science, Peking University, Beijing, China · LMAM and School of Mathematical Sciences, Peking University, Beijing, China +3
SLayerGen: a Crystal Generative Model for all Space and Layer Groups
May 7, 2026Rees Chang, Andrew Novick, Ryan P Adams +1Crystal Structure Generation Diffusion Models
Department of Materials Science & Engineering, University of Illinois at Urbana-Champaign · Department of Computer Science, Princeton University · Materials Research Laboratory, University of Illinois at Urbana-Champaign
VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials
Apr 30, 2026Rogério Almeida Gouvêa, Gian-Marco RignaneseCrystal Structure Prediction Materials Science
Institute of Condensed Matter and Nanosciences, Universit´e Catholique de Louvain, Louvain-la-Neuve,2026 Belgium. · WEL Research Institute, Avenue Pasteur 6, Wavre, Belgium.
Generative structure search for efficient and diverse discovery of molecular and crystal structures
Apr 30, 2026Yifang Qin, Yu Shi, Junfu Tan +3Crystal Structure Prediction Crystal Structure Generation
School of Computer Science, Peking University Beijing, 100871, China. · 2Zhongguancun Academy, Beijing, 100094, China. · School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 30332, Georgia, USA. +1
Crystal structure prediction using graph neural combinatorial optimization
Apr 27, 2026Stavros Gerolymatos, J. Kyle Brubaker, Martin J. A. Schuetz +1Graph Neural Networks Crystal Structure Prediction
School of Computer Science and Informatics, University of Liverpool, Liverpool, England, UK · Amazon Advanced Solutions Lab, Seattle, Washington 98170, USA · Arm Inc., San Jose, California 95134
Generative Discovery of Magnetic Insulators under Competing Physical Constraints
Apr 22, 2026Qiulin Zeng, Tahiya Chowdhury, Md Shafayat HossainMaterials Science Crystal Structure Generation
Department of Math, University of Southern California, Los Angeles, CA, 90007 · Department of Computer Science, Colby College, Waterville, ME, 04901 · Department of Materials Science and Engineering, University of California, Los Angeles, Los Angeles, CA 90095, United States +1
Finetuning-Free Diffusion Model with Adaptive Constraint Guidance for Inorganic Crystal Structure Generation
Apr 14, 2026Auguste de Lambilly, Vladimir Baturin, David Portehault +4Diffusion Model Guidance Crystal Structure Generation
CNRS-Saint-Gobain-NIMS, IRL 3629, Laboratory for Innovative Key Materials and Structures (LINK), 1-1 Namiki, 305-0044 Tsukuba, Japan · LTCI, Télécom Paris, Institut Polytechnique de Paris, France · Laboratoire de Chimie de la Matière Condensée de Paris (LCMCP), Sorbonne Université, CNRS, 4 place Jussieu, F-75005 Paris, France +2
Crystalite: A Lightweight Transformer for Efficient Crystal Modeling
Apr 2, 2026Tin Hadži Veljković, Joshua Rosenthal, Ivor Lončarić +1Diffusion Transformer Transformer
1UvA-Bosch Delta Lab · University of Amsterdam · 3Ruđer Bošković Institute
NextCrystal: a Symmetry-Driven Generative Framework for Crystal Structure Prediction
Feb 19, 2026Jinming Mu, Lixin He, Xudong Zhu +1Crystal Structure Prediction Crystal Structure Generation
Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, Anhui, China · Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, Anhui, China

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Composable Crystals: Controllable Materials Discovery via Concept Learning

Nian Liu, Yuwei Zeng, Ryoji Kubo, Nikita Kazeev+6

National University of Singapore · Loyola Marymount University

Vector-Quantized Variational Autoencoder Crystal Structure Generation

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De novo crystal generation, a central task in materials discovery, aims to generate crystals that are simultaneously valid, stable, unique, and novel. Existing methods mainly rely on black-box stochastic sampling, providing limited control over how generated structures move beyond the observed distribution. In this paper, we introduce a concept-based compositional framework for crystal generation. We train a vector-quantized variational autoencoder to automatically discover a shared set of reusable crystal concepts, which serve as building blocks for guided generation. These learned concepts naturally exhibit interpretability from both local atomic environments and global symmetry patterns, and generalize to crystals from different distributions. By recombining such concepts, our framework enables controllable exploration of novel crystals beyond the training distribution, rather than relying solely on unconstrained random sampling. To further improve composition efficiency, we introduce a composition generator and iteratively refine it using high-quality samples generated by the model itself. The resulting concept compositions are then used to condition downstream crystal generation. Numerical experiments on MP-20 and Alex-MP-20 show that compositing concepts separately increase base model up to 53.2% and 51.7% on V.S.U.N metric, with particular gains in novelty.

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