arXiv Machine Learning June 9, 2026 By Mianzhi Pan, JianFei Li, Peishuo Liu, Botian Wang, Yawen Ouyang, Yiming Rong, Hao Zhou, Jianbing Zhang

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

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

2d ago

Riemannian Flow Models with Reinforcement Learning for Molecular Crystal Structure Prediction

arXiv:2609.39773v1 Announce Type: new Abstract: Crystal structure governs material properties, making crystal structure prediction (CSP) a fundamental problem in materials science. Generative models...

By Thomas Egg, Harry Winston Sullivan, Maya M. Martirossyan, Philipp H\"ollmer, Cheng Zeng, Adrian Roitberg, Mingjie Liu, Richard Hennig, Sapna Sarupria, Ellad B. Tadmor, Stefano Martiniani

reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 30

Interpretable Inverse Design of Metal-Organic Frameworks with Large Language Model Agents

arXiv:2606. 29459v1 Announce Type: cross Abstract: Inverse design of metal-organic frameworks (MOFs) requires searching a combinatorially vast space where property labels are expensive and most machine-learning models reveal little about why a structure succeeds.

By Kyungmin Nam, Seunghee Han, Jihan Kim

llms agents

arXiv AI

Aug 13

Chemically Meaningful Textualization Enables Explainable Validation of Metal-Organic Frameworks by Large Language Models

arXiv:2608. 11283v1 Announce Type: cross Abstract: Computation-ready metal-organic framework (MOF) databases are essential for high-throughput screening, yet many reported crystal structures remain chemically unreasonable or disordered, compromising simulation fidelity.

By Guobin Zhao, Xiao-Yan Li

llms fine-tuning efficiency benchmarks safety

arXiv AI

Jun 9

MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science

arXiv:2606. 07712v1 Announce Type: cross Abstract: Progress in AI-driven crystal materials science has so far been carried by narrow architectures purpose-built for individual tasks -- graph neural networks for property prediction, diffusion and flow-matching models for crystal generation -- each excelling within its niche yet unable to act as a shared backbone across the full spectrum of materials problems.

By Zhan'ao Yao, Boxuan Zhang, Jingyuan Shu, Xiaoyu Wu, Rongyan Wang, Linjing Li, Dajun Zeng, Yudong Yao, Tingwei Chen, Youwei Wang, Xiaolin Zhao, Jiahui Shi, Jianjun Liu

llms diffusion reinforcement-learning

arXiv Machine Learning

Jun 3

Fast Organic Crystal Structure Prediction with Unit Cell Flow Matching

arXiv:2606. 03199v1 Announce Type: new Abstract: Organic crystal structure prediction (CSP) is a requirement for computational modelling of organic solids, but traditionally costs several CPU-years per molecule.

By Alston Lo, Luka Mucko, Austin H. Cheng, Andy Cai, Alastair J. A. Price, Wojciech Matusik, Al\'an Aspuru-Guzik

diffusion benchmarks safety

Hugging Face Trending Papers

Aug 27

Packora: Systematic Design for Generative Molecular Crystal Structure Prediction

Molecular crystal structure prediction (CSP) is important in pharmaceuticals, agrochemicals, and organic electronics, where subtle differences in molecular conformation and packing can strongly affect...

benchmarks

One email a morning, machine-written

Related stories

Riemannian Flow Models with Reinforcement Learning for Molecular Crystal Structure Prediction

Interpretable Inverse Design of Metal-Organic Frameworks with Large Language Model Agents

Chemically Meaningful Textualization Enables Explainable Validation of Metal-Organic Frameworks by Large Language Models

MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science

Fast Organic Crystal Structure Prediction with Unit Cell Flow Matching

Packora: Systematic Design for Generative Molecular Crystal Structure Prediction