arXiv AI

Interpretable Nanoporous Materials Design with Symmetry-Aware Networks

arXiv:2509. 15908v3 Announce Type: replace-cross Abstract: Nanoporous materials hold promise for diverse sustainable applications, yet their vast chemical space poses challenges for efficient design.

arXiv Machine Learning

Jun 3

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction

arXiv:2502. 02748v4 Announce Type: replace Abstract: Predicting properties of crystals from their structures is a fundamental yet challenging task in materials science.

By Jianan Nie, Peiyao Xiao, Kaiyi Ji, Peng Gao

benchmarks

arXiv AI

Aug 10

ED-CSP: Crystal Structure Prediction from Electron Diffraction

arXiv:2608. 06448v1 Announce Type: cross Abstract: Recovering a periodic 3D crystal structure from sparse, unindexed electron diffraction (ED) observations is a challenging generative inverse problem.

By Germain Poloudenny, Ya\"el Fr\'egier, Arnaud Demorti\`ere

benchmarks

arXiv AI

Jul 29

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks

arXiv:2607. 24818v1 Announce Type: cross Abstract: Accurate prediction of crystal properties remains a key challenge in computational materials science.

By Sanjay Chakraborty

benchmarks

arXiv Machine Learning

Jul 1

ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table

arXiv:2606. 30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species.

By Jacob W. Toney, Samir Darouich, Yiran Wang, Aaron G. Garrison, Johannes K\"astner, Heather J. Kulik

llms benchmarks

arXiv AI

Jun 30

Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark

arXiv:2606. 30170v1 Announce Type: cross Abstract: Generative molecular design is shaped by simple proxy benchmarks for drug-like properties and models pretrained on large pharmaceutical datasets.

By Matthias Blaschke, Daniel Kienzle, Zsuzsanna Koczor-Benda, Julian Lorenz, Rainer Lienhart, Fabian Pauly

benchmarks safety

arXiv AI

Jul 7

NextCrystal: a Symmetry-Driven Generative Framework for Crystal Structure Prediction

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

llms diffusion benchmarks

arXiv Machine Learning

Jun 2

Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design

arXiv:2606. 02507v1 Announce Type: cross Abstract: Inverse materials design is shifting materials discovery from forward prediction to targeted proposal of candidates that satisfy objectives under physical constraints.

By Anand Babu, Rog\'erio Almeida Gouv\^ea, Gian-Marco Rignanese

diffusion reinforcement-learning multimodal

arXiv AI

1d ago

Periodic Topological Deep Learning for Polymer Design and Discovery

arXiv:2605. 26833v2 Announce Type: replace-cross Abstract: Polymers underpin applications across energy, healthcare, and materials science, yet their vast chemical space makes systematic discovery challenging.

By Yasharth Yadav, Tze Kwang Gerald Er, Atsushi Goto, Kelin Xia

benchmarks

arXiv AI

Jul 9

Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning

arXiv:2607. 07708v1 Announce Type: cross Abstract: Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and periodic organization.

By Chen Tang, Yizhou Wang, Jianyu Wu, Lintao Wang, Shixiang Tang, Pengze Li, Encheng Su, Jun Yao, Jiabei Xiao, Yuqi Shi, Jielan Li, Hongxia Hao, Zhangyang Gao, Fang Wu, Ben Fei, Xiangyu Yue, Pan Tan, Bozitao Zhong, Jinouwen Zhang, Aoran Wang, Yan Lu, Jiaheng Liu, Xinzhu Ma, Liang Hong, Mingyue Zheng, Phil Torr, Bowen Zhou, Wanli Ouyang, Lei Bai

llms multimodal benchmarks

arXiv AI

Jul 13

Model Agnostic Graph Prompt Learning for Crystal Property Prediction

arXiv:2607. 08996v1 Announce Type: cross Abstract: Graph Neural Networks have emerged as a powerful tool for the fast and accurate prediction of various crystal properties.

By Shrimon Mukherjee, Kishalay Das, Partha Basuchowdhuri, Pawan Goyal, Niloy Ganguly

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

benchmarks safety

arXiv Machine Learning

Jun 17

Adaptable Method for Crystal Design across Diverse Constraints and Objectives with Pretrained Property Predictors

arXiv:2410. 08562v5 Announce Type: replace-cross Abstract: Advanced crystal design can accelerate materials discovery across applications from photovoltaics to spintronics.

By Akihiro Fujii, Yoshitaka Ushiku, Koji Shimizu, Anh Khoa Augustin Lu, Satoshi Watanabe

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