arXiv AI
Sep 11

uFlowCSP: Crystal Structure Prediction using Mean flow generative models

uFlowCSP is a MeanFlow-based crystal structure prediction model that learns the average probability‑flow velocity, enabling it to generate complete crystal structures in one to five network evaluations. It achieves inference speeds 5×–58× faster than diffusion and flow‑matching methods while matching or surpassing their performance, with a chemistry‑ and symmetry‑aware Transformer that uses canonical atom ordering and per‑token chemistry embeddings. On the MP‑20 benchmark, uFlowCSP attains comparable or higher accuracy with far fewer evaluations and significantly lower wall‑clock time, demonstrating improved accuracy per network evaluation.

By Sourin Dey, Dipannoy Das Gupta, Lai Wei, Sadman Sadeed Omee, Jianjun Hu
arXiv Machine Learning
Aug 28

Packora: Systematic Design for Generative Molecular Crystal Structure Prediction

Packora is a flow-based generative model designed for molecular crystal structure prediction (CSP). It jointly predicts atomic coordinates and lattice parameters from molecular graphs, supporting multi-component and organometallic crystals and allowing conditioning on conformers, stereochemistry, and space-group data. In evaluations inspired by the CCDC CSP blind test, Packora outperforms baselines on generation and ranking benchmarks, achieving superior matched-budget coverage, higher experimental-form recovery, lower ranks, and faster convergence.

By Nayoung Kim, Kiyoung Seong, Sungsoo Ahn
arXiv Machine Learning
Jun 15

LapidaryEngine: Fully Conversational Crystal Generation

arXiv:2606. 14215v1 Announce Type: new Abstract: The emergence of Large Language Models (LLMs) has inspired the vision of generating bespoke crystal materials directly from natural-language instructions, enabling users to design materials through intuitive, conversational interaction.

By Yusei Ito, Yuta Suzuki, Tomoya Murata, Masaki Adachi
arXiv Machine Learning
Jun 9

Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction

arXiv:2601. 09285v2 Announce Type: replace Abstract: Metal-organic frameworks (MOFs) are porous crystalline materials with broad applications such as carbon capture and drug delivery, yet accurately predicting their 3D structures remains a significant challenge.

By Mianzhi Pan, JianFei Li, Peishuo Liu, Botian Wang, Yawen Ouyang, Yiming Rong, Hao Zhou, Jianbing Zhang