Orbitol is a crystal generation model that uses Wyckoff-orbit tokens instead of atom tokens, enabling direct control over the space group of generated structures. It produces structures two to three orders of magnitude faster than symmetry‑aware baselines and achieves higher success rates in generating stable, novel structures that retain the requested symmetry. The authors also introduce SPARC, a score combining space‑group retention with distribution agreement, and demonstrate Orbitol’s effectiveness in generating high‑quality piezoelectric candidates with superior figures of merit.
By Ivan \v{Z}ugec, Joshua Rosenthal, Ivor Lon\v{c}ari\'c, Jan-Willem van de Meent, Tin Had\v{z}i Veljkovi\'c
arXiv:2608. 07401v1 Announce Type: cross Abstract: The search for new crystalline materials spans an enormous compositional and structural space.
By Zhuotao Jin, Xiaoyun Wang, Nicholas Brawand, Roman Zubatyuk, Atul Thakur, Eric Qu, Boris Kozinsky, Justin Smith
arXiv:2610.27762v1 Announce Type: new
Abstract: Deep generative modeling has achieved remarkable progress in crystal generation. Given the rich symmetries inherent in crystalline structures, recent w...
By Ziqiao Meng, Rongsheng Chen, Zhi Zheng, Nikita Kazeev, Zhenkun Wang, Andrey Ustyuzhanin, Wee Sun Lee
The paper reports that autonomous agents can generate, test, and refute millions of candidate structure-plausibility laws, ultimately producing eight Plausibility Rules for Inorganic Structures (PRIS) that capture five key mechanisms of crystal stability. PRIS achieves high agreement with experimental structures (82–99%) and outperforms traditional Pauling rules, detecting damaged crystals with 87.9% accuracy and correlating strongly with synthesizability. By integrating PRIS with a synthesis score (PSS), the authors demonstrate significant reductions in expensive DFT validation and improved inverse-design efficiency, while also providing chemical explanations for failures and anomalies in crystal data.
By Zhilong Song, Lixue Cheng
arXiv:2602. 17176v4 Announce Type: replace-cross Abstract: Crystal structure prediction (CSP), which aims to predict the 3D atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understanding.
By Jinming Mu, Lixin He, Xudong Zhu, Shi Yin
Crystal generators and tool-using agents propose structures faster than density functional theory (DFT) energy and phonon calculations or experiments can assess them. Deciding which candidates merit e...
SymADiT is a symmetry‑aware variant of the All‑atom Diffusion Transformer (ADiT) designed for crystal generation. It uses Wyckoff‑based crystal representations and enforces symmetry constraints from the crystal’s space group and atom positions during latent‑space generative modeling. The approach yields stable, symmetric, and physically plausible crystals while maintaining competitive performance against both symmetry‑aware and symmetry‑agnostic models.
By Anmar Karmush, Cedric Mathieu Brandenburg, Soheil Ershadrad, Johanna Rosen, Michael Felsberg, Filip Ekstr\"om Kelvinius
arXiv:2608. 13457v1 Announce Type: new Abstract: Generating crystals has recently attracted significant interest due to their broad applications in materials science.
By Van Khoa Nguyen, Alexandros Kalousis
The paper introduces CrystAF, an all‑atom crystal flow‑map generation model, and evaluates where physics should be incorporated into generative crystal structure models. By applying physics‑informed post‑training, the authors improve molecular validity and crystal packing without altering sampling speed, while inference‑time corrections further refine the structures. The study demonstrates that post‑training and inference‑time physics are complementary, and that the post‑training approach transfers to other generators such as Clari‑M and MolCrystalFlow.
By Haocheng Tang, Junmei Wang, Wengong Jin
arXiv:2610.32678v1 Announce Type: cross
Abstract: Crystal materials are designed either by screening known crystals for a target property or by generating new crystals with that property. Both routes...
By Zhilong Song, Zijun Pang, Lixue Cheng
CrystalReasoner (CrysReas) is an end‑to‑end large language model framework that generates crystal structures from natural language instructions by incorporating physical priors as thinking tokens—such as crystallographic symmetry, local coordination, and predicted properties—before producing atomic coordinates. It then uses reinforcement learning with a multi‑objective reward to enforce physical validity, chemical consistency, and thermodynamic stability, and includes task‑specific rewards for property‑conditioned generation. Empirical results show that CrysReas outperforms prior methods, triples the S.U.N. ratio, and adapts reasoning length with increasing atom counts.
By Yuyang Wu, Stefano Falletta, Delia McGrath, Sherry Yang
arXiv:2608. 15900v1 Announce Type: cross Abstract: Data-driven materials discovery interpolates more reliably than it extrapolates and seldom reaches new structure types.
By Dinh-Khiet Le, Minh-Quyet Ha, Hong-Phuc Vu-Dinh, Takashi Miyake, Hiori Kino, Hieu-Chi Dam