Saved in:
| Main Authors: | Nie, Jianan, Xiao, Peiyao, Ji, Kaiyi, Gao, Peng |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.02748 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Reciprocal Space Attention for Learning Long-Range Interactions
by: Ramasubramanian, Hariharan, et al.
Published: (2025)
by: Ramasubramanian, Hariharan, et al.
Published: (2025)
Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation
by: Yan, Keqiang, et al.
Published: (2025)
by: Yan, Keqiang, et al.
Published: (2025)
Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks
by: Peng, Jiayu, et al.
Published: (2024)
by: Peng, Jiayu, et al.
Published: (2024)
Achieving ${O}(ε^{-1.5})$ Complexity in Hessian/Jacobian-free Stochastic Bilevel Optimization
by: Yang, Yifan, et al.
Published: (2023)
by: Yang, Yifan, et al.
Published: (2023)
Generative Models for Crystalline Materials
by: Metni, Houssam, et al.
Published: (2025)
by: Metni, Houssam, et al.
Published: (2025)
Synthesizability Prediction of Crystalline Structures with a Hierarchical Transformer and Uncertainty Quantification
by: Ebrahimzadeh, Danial, et al.
Published: (2025)
by: Ebrahimzadeh, Danial, et al.
Published: (2025)
Space Group Informed Transformer for Crystalline Materials Generation
by: Cao, Zhendong, et al.
Published: (2024)
by: Cao, Zhendong, et al.
Published: (2024)
Complete and Efficient Graph Transformers for Crystal Material Property Prediction
by: Yan, Keqiang, et al.
Published: (2024)
by: Yan, Keqiang, et al.
Published: (2024)
CrysToGraph: A Comprehensive Predictive Model for Crystal Materials Properties and the Benchmark
by: Wang, Hongyi, et al.
Published: (2024)
by: Wang, Hongyi, et al.
Published: (2024)
LDC-MTL: Balancing Multi-Task Learning through Scalable Loss Discrepancy Control
by: Xiao, Peiyao, et al.
Published: (2025)
by: Xiao, Peiyao, et al.
Published: (2025)
ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction
by: Huang, Jiao, et al.
Published: (2024)
by: Huang, Jiao, et al.
Published: (2024)
Multimodal Foundation Models for Material Property Prediction and Discovery
by: Moro, Viggo, et al.
Published: (2023)
by: Moro, Viggo, et al.
Published: (2023)
MechProNet: Machine Learning Prediction of Mechanical Properties in Metal Additive Manufacturing
by: Akbari, Parand, et al.
Published: (2022)
by: Akbari, Parand, et al.
Published: (2022)
Compositional Representation of Polymorphic Crystalline Materials
by: Lee, Namkyeong, et al.
Published: (2023)
by: Lee, Namkyeong, et al.
Published: (2023)
AlloyBERT: Alloy Property Prediction with Large Language Models
by: Chaudhari, Akshat, et al.
Published: (2024)
by: Chaudhari, Akshat, et al.
Published: (2024)
Regression with Large Language Models for Materials and Molecular Property Prediction
by: Jacobs, Ryan, et al.
Published: (2024)
by: Jacobs, Ryan, et al.
Published: (2024)
Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes
by: Alvi, Sk Md Ahnaf Akif, et al.
Published: (2025)
by: Alvi, Sk Md Ahnaf Akif, et al.
Published: (2025)
Neutron and X-ray Diffraction Reveal the Limits of Long-Range Machine Learning Potentials for Medium-Range Order in Silica Glass
by: Balantrapu, Sai Harshit, et al.
Published: (2026)
by: Balantrapu, Sai Harshit, et al.
Published: (2026)
MGDA Converges under Generalized Smoothness, Provably
by: Zhang, Qi, et al.
Published: (2024)
by: Zhang, Qi, et al.
Published: (2024)
DeepMTL2R: A Library for Deep Multi-task Learning to Rank
by: Dong, Chaosheng, et al.
Published: (2026)
by: Dong, Chaosheng, et al.
Published: (2026)
QT-Net: Rethinking Evaluation of AI Models in Atomic Chemical Space
by: Crespo, Pablo Martínez, et al.
Published: (2026)
by: Crespo, Pablo Martínez, et al.
Published: (2026)
Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE Model
by: Slautin, Boris N., et al.
Published: (2025)
by: Slautin, Boris N., et al.
Published: (2025)
Role of Large Language Models and Retrieval-Augmented Generation for Accelerating Crystalline Material Discovery: A Systematic Review
by: Oche, Agada Joseph, et al.
Published: (2025)
by: Oche, Agada Joseph, et al.
Published: (2025)
Tensor Completion for Surrogate Modeling of Material Property Prediction
by: Pakala, Shaan, et al.
Published: (2025)
by: Pakala, Shaan, et al.
Published: (2025)
Theoretical Study of Conflict-Avoidant Multi-Objective Reinforcement Learning
by: Wang, Yudan, et al.
Published: (2024)
by: Wang, Yudan, et al.
Published: (2024)
Benchmarking GNNs for OOD Materials Property Prediction with Uncertainty Quantification
by: Tan, Liqin, et al.
Published: (2025)
by: Tan, Liqin, et al.
Published: (2025)
Data Fusion of Deep Learned Molecular Embeddings for Property Prediction
by: Appleton, Robert J, et al.
Published: (2025)
by: Appleton, Robert J, et al.
Published: (2025)
CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction
by: Mukherjee, Shrimon, et al.
Published: (2024)
by: Mukherjee, Shrimon, et al.
Published: (2024)
Reduced Order Modeling of Energetic Materials Using Physics-Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC)
by: Gray, Zoë J., et al.
Published: (2025)
by: Gray, Zoë J., et al.
Published: (2025)
Polymorphism Crystal Structure Prediction with Adaptive Space Group Diversity Control
by: Omee, Sadman Sadeed, et al.
Published: (2025)
by: Omee, Sadman Sadeed, et al.
Published: (2025)
Analyzing Heat Transport in Crystalline Polymers in Real and Reciprocal Space
by: Reicht, Lukas, et al.
Published: (2025)
by: Reicht, Lukas, et al.
Published: (2025)
MoMa: A Modular Deep Learning Framework for Material Property Prediction
by: Wang, Botian, et al.
Published: (2025)
by: Wang, Botian, et al.
Published: (2025)
Graph Neural Network Prediction of Nonlinear Optical Properties
by: Alkabakibi, Yomn, et al.
Published: (2025)
by: Alkabakibi, Yomn, et al.
Published: (2025)
Supervised Pretraining for Material Property Prediction
by: Rahman, Chowdhury Mohammad Abid, et al.
Published: (2025)
by: Rahman, Chowdhury Mohammad Abid, et al.
Published: (2025)
BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models
by: Antoniuk, Evan R., et al.
Published: (2025)
by: Antoniuk, Evan R., et al.
Published: (2025)
Physically-Constrained Autoencoder-Assisted Bayesian Optimization for Refinement of High-Dimensional Defect-Sensitive Single Crystalline Structure
by: Agada, Joseph Oche, et al.
Published: (2025)
by: Agada, Joseph Oche, et al.
Published: (2025)
Bridging Theory and Experiment in Materials Discovery: Machine-Learning-Assisted Prediction of Synthesizable Structures
by: Xin, Yu, et al.
Published: (2025)
by: Xin, Yu, et al.
Published: (2025)
Flow Matching for Accelerated Simulation of Atomic Transport in Crystalline Materials
by: Nam, Juno, et al.
Published: (2024)
by: Nam, Juno, et al.
Published: (2024)
Leakage-Aware Bandgap Prediction on the JARVIS-DFT Dataset: A Phase-Wise Feature Analysis
by: Sharma, Gaurav Kumar
Published: (2025)
by: Sharma, Gaurav Kumar
Published: (2025)
Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties with Phonon-Informed Datasets
by: Benítez, Pol, et al.
Published: (2025)
by: Benítez, Pol, et al.
Published: (2025)
Similar Items
-
Reciprocal Space Attention for Learning Long-Range Interactions
by: Ramasubramanian, Hariharan, et al.
Published: (2025) -
Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation
by: Yan, Keqiang, et al.
Published: (2025) -
Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks
by: Peng, Jiayu, et al.
Published: (2024) -
Achieving ${O}(ε^{-1.5})$ Complexity in Hessian/Jacobian-free Stochastic Bilevel Optimization
by: Yang, Yifan, et al.
Published: (2023) -
Generative Models for Crystalline Materials
by: Metni, Houssam, et al.
Published: (2025)