arXiv AI

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.

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 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

Aug 3

Ordered-to-disordered transfer learning with graph neural networks for formation-energy and HOMO-LUMO gap prediction in high-entropy perovskite oxides

arXiv:2607. 29510v1 Announce Type: cross Abstract: High-entropy perovskite oxides (HEPOs) represent a chemically complex class of materials with promising functional properties, yet their vast compositional space and, chemical/structural disorder pose significant challenge for accurate property prediction.

By Panupol Untarabut, Narjes Jomaa, Sylvian Cadars, Olivier Masson, Samuel Bernard, Assil Bouzid, Santanu Saha

fine-tuning

arXiv Machine Learning

Jul 9

Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory

arXiv:2604. 09320v2 Announce Type: replace-cross Abstract: Mechanistic understanding and rational design of complex chemical systems depend on fast and accurate predictions of electronic structures beyond individual building blocks.

By Siqi Chen, Zhiqiang Wang, Yili Shen, Xianqi Deng, Xi Cheng, Cheng-Wei Ju, Jun Yi, Guo Ling, Dieaa Alhmoud, Hui Guan, Zhou Lin

fine-tuning benchmarks safety

arXiv AI

Jun 30

Optimizing Expert-Designed Crystal Graph Networks for Band-Gap Prediction with an Autonomous LLM Research Loop

arXiv:2606. 29717v1 Announce Type: cross Abstract: Predicting a material's properties from its structure is a central, fast-advancing problem in computational materials science.

By Chenmu Zhang, Boris I. Yakobson

llms rag agents benchmarks

arXiv Machine Learning

Jun 2

Latent Diffusion Pretraining for Crystal Property Prediction

arXiv:2606. 00776v1 Announce Type: new Abstract: Fast and accurate prediction of crystal properties is a central challenge in new materials design.

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

llms diffusion fine-tuning

arXiv AI

1d ago

BDIP-Net: Dual-Interaction Graph Learning for Property Prediction of Bilayer Materials

arXiv:2608. 14640v1 Announce Type: cross Abstract: Stacked bilayer materials exhibit rich stacking-dependent properties driven by the interplay between strong intra-layer bonding and weak inter-layer van der Waals interactions.

By An Vuong, Chen Zhao, Jin Hu, Shui-Qing Yu, Xintao Wu

arXiv Machine Learning

Jul 8

Multimodal Molecular Representation Learning with Graph Neural Networks, Deep & Cross Networks, and SMILES Embeddings

arXiv:2607. 05736v1 Announce Type: new Abstract: Molecular property prediction often relies on isolated data modalities, where continuous 3D graph neural networks (GNNs) struggle to efficiently capture long-range topological dependencies and exact macroscopic heuristics.

By Qiwei Han, Chi Zhou, Ruobing Wang, Zheng Ma

rag diffusion efficiency multimodal benchmarks safety

arXiv AI

Jul 7

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.

By Zhenhao Zhou, Salman Bin Kashif, Jin-Hu Dou, Chris Wolverton, Kaihang Shi, Tao Deng, Zhenpeng Yao

benchmarks safety

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 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 Machine Learning

Jun 19

MolGraphBench: A Benchmark of GNN Architectures for Molecular Regression Tasks

arXiv:2602. 20573v3 Announce Type: replace Abstract: Molecules are often represented as SMILES strings, which can be readily converted to hand-crafted descriptors or fingerprints (FP) for molecular property prediction.

By Rajan, Ishaan Gupta

fine-tuning benchmarks

Related stories