Cover Image

Complete and Efficient Graph Transformers for Crystal Material Property Prediction

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yan, Keqiang, Fu, Cong, Qian, Xiaofeng, Qian, Xiaoning, Ji, Shuiwang
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Materials Science
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction
by: Yan, Keqiang, et al.
Published: (2024)

Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation
by: Yan, Keqiang, et al.
Published: (2025)

A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures
by: Yan, Keqiang, et al.
Published: (2025)

Orbital Transformers for Predicting Wavefunctions in Time-Dependent Density Functional Theory
by: Zhang, Xuan, et al.
Published: (2026)

Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists
by: Zhou, Lianhao, et al.
Published: (2025)

ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction
by: Huang, Jiao, et al.
Published: (2024)

Predicting Many Crystal Properties via an Adaptive Transformer-based Framework
by: Xu, Haosheng, et al.
Published: (2024)

CrysToGraph: A Comprehensive Predictive Model for Crystal Materials Properties and the Benchmark
by: Wang, Hongyi, et al.
Published: (2024)

Tensor Completion for Surrogate Modeling of Material Property Prediction
by: Pakala, Shaan, et al.
Published: (2025)

Bridging the Semantic-Numerical Gap: A Numerical Reasoning Method of Cross-modal Knowledge Graph for Material Property Prediction
by: Song, Guangxuan, et al.
Published: (2023)

CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction
by: Mukherjee, Shrimon, et al.
Published: (2024)

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
by: Yu, Haiyang, et al.
Published: (2023)

Multimodal Foundation Models for Material Property Prediction and Discovery
by: Moro, Viggo, et al.
Published: (2023)

Material Property Prediction with Element Attribute Knowledge Graphs and Multimodal Representation Learning
by: Huang, Chao, et al.
Published: (2024)

Graph Transformer Networks for Accurate Band Structure Prediction: An End-to-End Approach
by: Gong, Weiyi, et al.
Published: (2024)

Regression with Large Language Models for Materials and Molecular Property Prediction
by: Jacobs, Ryan, et al.
Published: (2024)

Benchmarking GNNs for OOD Materials Property Prediction with Uncertainty Quantification
by: Tan, Liqin, et al.
Published: (2025)

CTGNN: Crystal Transformer Graph Neural Network for Crystal Material Property Prediction
by: Du, Zijian, et al.
Published: (2024)

Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames
by: Yu, Haiyang, et al.
Published: (2025)

Supervised Pretraining for Material Property Prediction
by: Rahman, Chowdhury Mohammad Abid, et al.
Published: (2025)

MoMa: A Modular Deep Learning Framework for Material Property Prediction
by: Wang, Botian, et al.
Published: (2025)

Graph Neural Network Prediction of Nonlinear Optical Properties
by: Alkabakibi, Yomn, et al.
Published: (2025)

Learning ORDER-Aware Multimodal Representations for Composite Materials Design
by: Li, Xinyao, et al.
Published: (2026)

Equivariant Diffusion for Crystal Structure Prediction
by: Lin, Peijia, et al.
Published: (2025)

MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching
by: Zeng, Cheng, et al.
Published: (2026)

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction
by: Nie, Jianan, et al.
Published: (2025)

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties with Phonon-Informed Datasets
by: Benítez, Pol, et al.
Published: (2025)

Crystal Structure Prediction by Joint Equivariant Diffusion
by: Jiao, Rui, et al.
Published: (2023)

AMShortcut: An Inference- and Training-Efficient Inverse Design Model for Amorphous Materials
by: Lin, Yan, et al.
Published: (2026)

Bridging Text and Crystal Structures: Literature-driven Contrastive Learning for Materials Science
by: Suzuki, Yuta, et al.
Published: (2025)

A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation
by: Solé, Àlex, et al.
Published: (2025)

Generative Inverse Design of Crystal Structures via Diffusion Models with Transformers
by: Takahara, Izumi, et al.
Published: (2024)

Hybrid Quantum Graph Neural Network for Molecular Property Prediction
by: Vitz, Michael, et al.
Published: (2024)

Advancing Extrapolative Predictions of Material Properties through Learning to Learn
by: Noda, Kohei, et al.
Published: (2024)

Multi-Task Multi-Fidelity Learning of Properties for Energetic Materials
by: Appleton, Robert J., et al.
Published: (2024)

Predicting Atomistic Transitions with Transformers
by: Tischler, Henry, et al.
Published: (2026)

OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction
by: Jin, Emily, et al.
Published: (2025)

Polymorphism Crystal Structure Prediction with Adaptive Space Group Diversity Control
by: Omee, Sadman Sadeed, et al.
Published: (2025)

ContinuouSP: Generative Model for Crystal Structure Prediction with Invariance and Continuity
by: Tone, Yuji, et al.
Published: (2025)

OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs
by: Vahediahmar, Mohammadmahdi, et al.
Published: (2026)