arXiv Machine Learning July 7, 2026 By Vahe Gharakhanyan, Yi Yang, Luis Barroso-Luque, Daniel S. Levine, Sushree Jagriti Sahoo, Brandon M. Wood, Kyle Michel, Muhammed Shuaibi, Gregory J. O. Beran, Viachaslau Bernat, Misko Dzamba, Xiang Fu, Meng Gao, Xingyu Liu, Benjamin K. Miller, Keian Noori, Lafe J. Purvis, Tingling Rao, Ammar Rizvi, Matt Uyttendaele, Andrew J. Ouderkirk, Chiara Daraio, C. Lawrence Zitnick, Arman Boromand, Noa Marom, Zachary W. Ulissi, Anuroop Sriram

FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms

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arXiv:2508. 02641v2 Announce Type: replace-cross Abstract: Molecular crystal structure prediction (CSP) is essential for applications in pharmaceuticals and organic electronics.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 28

MolCryst-MLIPs: A Machine-Learned Interatomic Potentials Database for Molecular Crystals

arXiv:2604. 13897v2 Announce Type: replace Abstract: We present an open Molecular Crystal (MC) database of Machine-Learned Interatomic Potentials (MLIP) called MolCryst-MLIPs.

By Adam Lahouari, Shen Ai, Jihye Han, Jillian Hoffstadt, Philipp Hoellmer, Charlotte Infante, Pulkita Jain, Sangram Kadam, Maya M. Martirossyan, Amara McCune, Hypatia Newton, Shlok J. Paul, Willmor Pena, Jonathan Raghoonanan, Sumon Sahu, Oliver Tan, Andrea Vergara, Jutta Rogal, Mark E. Tuckerman

fine-tuning benchmarks

arXiv Machine Learning

Jul 31

APO: Unsupervised Atomic Policy Optimization for 3D Structure Prediction of Atomic Systems

arXiv:2607. 28553v1 Announce Type: new Abstract: Predicting the 3D structures of atomic systems is fundamental to advancing material science and drug discovery.

By Shentong Mo, Yatao Bian

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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

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

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arXiv Machine Learning

Jun 2

Benchmark Dataset for Catalysis on 2D MXenes

arXiv:2606. 00794v1 Announce Type: cross Abstract: Merging first-principles calculations with machine learning (ML), we aim to accelerate the exploration of catalytic behaviour in novel materials.

By Pavlo Melnyk, Anmar Karmush, M{\aa}rten Wadenb\"ack, Ania Beatriz Rodr\'iguez-Barrera, Johanna Rosen, Michael Felsberg, Jonas Bj\"ork

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arXiv AI

Jun 12

SupraBench: A Benchmark for Supramolecular Chemistry

arXiv:2606. 13477v1 Announce Type: cross Abstract: Supramolecular chemistry, which includes the study of non-covalent host-guest assemblies, has advanced various applications.

By Tianyi Ma, Yijun Ma, Zehong Wang, Weixiang Sun, Ziming Li, Connor R. Schmidt, Chuxu Zhang, Matthew J. Webber, Yanfang Ye

llms fine-tuning benchmarks

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