Copertina

Universal and Experiment-calibrated Prediction of XANES through Crystal Graph Neural Network and Transfer Learning Strategy

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Lin, Zichang, Chen, Wenjie, Lin, Yitao, Zhang, Xinxin, Zhang, Yuegang
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Materials Science
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

AI-Driven Accelerated Discovery of Intercalation-type Cathode Materials for Magnesium Batteries
di: Chen, Wenjie, et al.
Pubblicazione: (2024)

ChemGraph-XANES: An Agentic Framework for XANES Simulation and Analysis
di: Grizzi, Vitor F., et al.
Pubblicazione: (2026)

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

XANE(3): An E(3)-Equivariant Graph Neural Network for Accurate Prediction of XANES Spectra from Atomic Structures
di: Grizzi, Vitor F., et al.
Pubblicazione: (2026)

Equivariant Graph Neural Networks for Prediction of Tensor Material Properties of Crystals
di: Heilman, Alex, et al.
Pubblicazione: (2024)

Scalable Dielectric Tensor Predictions for Inorganic Materials using Equivariant Graph Neural Networks
di: Hua, Haowei, et al.
Pubblicazione: (2026)

SR-CGCNN: Shared Recurrent Convolution in Crystal Graph Neural Networks for Materials Property Prediction
di: Bhattacharjee, Satadeep
Pubblicazione: (2026)

Crystal Transformer Based Universal Atomic Embedding for Accurate and Transferable Prediction of Materials Properties
di: Jin, Luozhijie, et al.
Pubblicazione: (2024)

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

Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structure
di: Hung, Nguyen Tuan, et al.
Pubblicazione: (2024)

Crystal Fractional Graph Neural Network for Energy Prediction of High-Entropy Alloys
di: Kotama, Takanori, et al.
Pubblicazione: (2026)

In Situ Solid Electrolyte Ionic Pathway Formation in High Sulfur Loading Cathodes for High‐Performance All‐Solid‐State Lithium–Sulfur Batteries (Adv. Energy Mater. 22/2025)
di: Yipeng Su, et al.
Pubblicazione: (2025)

In Situ Solid Electrolyte Ionic Pathway Formation in High Sulfur Loading Cathodes for High‐Performance All‐Solid‐State Lithium–Sulfur Batteries
di: Yipeng Su, et al.
Pubblicazione: (2025)

Learning Thermoelectric Transport from Crystal Structures via Multiscale Graph Neural Network
di: Zeng, Yuxuan, et al.
Pubblicazione: (2025)

Efficient Crystal Structure Prediction Using Universal Neural Network Potential with Diversity Preservation in Genetic Algorithms
di: Shibayama, Takuya, et al.
Pubblicazione: (2025)

Decoding Dopant-Induced Electronic Modulation in Graphene via Region-Resolved Machine Learning of XANES
di: Wang, Yinan, et al.
Pubblicazione: (2026)

CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models
di: Zhang, Chengqian, et al.
Pubblicazione: (2026)

PDDFormer: Pairwise Distance Distribution Graph Transformer for Crystal Material Property Prediction
di: Shen, Xiangxiang, et al.
Pubblicazione: (2024)

Probing the Limit of Heat Transfer in Inorganic Crystals with Deep Learning
di: Li, Jielan, et al.
Pubblicazione: (2025)

Beyond Predicted ZT: Machine Learning Strategies for the Experimental Discovery of Thermoelectric Materials
di: Athar, Shoeb, et al.
Pubblicazione: (2026)

Multi-Source Domain Transfer Learning for Accurate Property Prediction in Two-Dimensional Materials
di: Zhang, Huiyang, et al.
Pubblicazione: (2026)

An Efficient High-Degree, High-Order Equivariant Graph Neural Network for Direct Crystal Structure Optimization
di: Yang, Ziduo, et al.
Pubblicazione: (2026)

A First Experiment at Interfacing Crystal Plasticity and Continuum Dislocation Dynamics
di: SharafEldin, Khaled, et al.
Pubblicazione: (2021)

Transferable 3D Convolutional Neural Networks for Elastic Constants Prediction in Nanoporous Metals
di: Zorkaltsev, Sergei, et al.
Pubblicazione: (2026)

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

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

Experimental Powder X-ray Diffraction Crystal Structure Determination with RealPXRD-Solver
di: Li, Qi, et al.
Pubblicazione: (2026)

Do Graph Neural Networks Work for High Entropy Alloys?
di: Zhang, Hengrui, et al.
Pubblicazione: (2024)

Decoding the Electronic and Structural Fingerprints of Single-Atom Catalysts via DFT-Assisted XANES Analysis
di: Lazar, Petr, et al.
Pubblicazione: (2026)

K-edge XANES of octahedral aluminum compounds: similarities and differences via the analysis of excitonic properties
di: Amoyaw, Newman, et al.
Pubblicazione: (2024)

EOSnet: Embedded Overlap Structures for Graph Neural Networks in Predicting Material Properties
di: Tao, Shuo, et al.
Pubblicazione: (2024)

Learning the physics-consistent material behavior from experimentally measurable data via PDE-constrained optimization
di: Wu, Xinxin, et al.
Pubblicazione: (2024)

A New Workflow for Materials Discovery Bridging the Gap Between Experimental Databases and Graph Neural Networks
di: Schoener, Brandon, et al.
Pubblicazione: (2026)

Accurate Prediction of Tensorial Spectra Using Equivariant Graph Neural Network
di: Hsu, Ting-Wei, et al.
Pubblicazione: (2025)

Predicting the Curie Temperature of Magnetic Materials with Machine Learning: Descriptor Engineering, Graph Neural Networks, and the Role of Curated Data
di: Orang, Akram Abedi, et al.
Pubblicazione: (2025)

Decoupling Coherent and Particle-like Phonon Transport through Bonding Hierarchy in Soft Superionic Crystals
di: Xiong, Wenjie, et al.
Pubblicazione: (2025)

Towards Accurate Prediction of Configurational Disorder Properties in Materials using Graph Neural Networks
di: Fang, Zhenyao, et al.
Pubblicazione: (2024)

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

Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling
di: Li, Chaojian, et al.
Pubblicazione: (2025)

Review of the Electrochemical Double Layer Theory and Its Applications in Battery Electrode Materials
di: He, Yitao
Pubblicazione: (2025)