Materials design based on a material-motif network and heterogeneous graphs
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | Aryal, Anoj, Gong, Weiyi, Banjade, Huta, Yan, Qimin |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
Symmetry-guided data-driven discovery of native quantum defects in two-dimensional materials
par: Tsai, Jeng-Yuan, et autres
Publié: (2024)
par: Tsai, Jeng-Yuan, et autres
Publié: (2024)
Bridging deep learning force fields and electronic structures with a physics-informed approach
par: Qi, Yubo, et autres
Publié: (2024)
par: Qi, Yubo, et autres
Publié: (2024)
Crystal Hypergraph Convolutional Networks
par: Heilman, Alexander J., et autres
Publié: (2024)
par: Heilman, Alexander J., et autres
Publié: (2024)
Towards Accurate Prediction of Configurational Disorder Properties in Materials using Graph Neural Networks
par: Fang, Zhenyao, et autres
Publié: (2024)
par: Fang, Zhenyao, et autres
Publié: (2024)
Graph Transformer Networks for Accurate Band Structure Prediction: An End-to-End Approach
par: Gong, Weiyi, et autres
Publié: (2024)
par: Gong, Weiyi, et autres
Publié: (2024)
Database of Tensorial Optical and Transport Properties of Materials From the Wannier Function Method
par: Fang, Zhenyao, et autres
Publié: (2025)
par: Fang, Zhenyao, et autres
Publié: (2025)
A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials
par: Fang, Zhenyao, et autres
Publié: (2025)
par: Fang, Zhenyao, et autres
Publié: (2025)
Chemical-motif characterization of short-range order with E(3)-equivariant graph neural networks
par: Sheriff, Killian, et autres
Publié: (2024)
par: Sheriff, Killian, et autres
Publié: (2024)
Equivariant Graph Neural Networks for Prediction of Tensor Material Properties of Crystals
par: Heilman, Alex, et autres
Publié: (2024)
par: Heilman, Alex, et autres
Publié: (2024)
Coarse-grained crystal graph neural networks for reticular materials design
par: Korolev, Vadim, et autres
Publié: (2023)
par: Korolev, Vadim, et autres
Publié: (2023)
Leveraging Persistent Homology Features for Accurate Defect Formation Energy Predictions via Graph Neural Networks
par: Fang, Zhenyao, et autres
Publié: (2024)
par: Fang, Zhenyao, et autres
Publié: (2024)
Effect of size dependent strain at various coverage on island formation: Kinetic Monte Carlo study
par: Aryal, Ganesh
Publié: (2024)
par: Aryal, Ganesh
Publié: (2024)
Inverse design of heterodeformations for strain soliton networks in bilayer 2D materials
par: Ahmed, Md Tusher, et autres
Publié: (2026)
par: Ahmed, Md Tusher, et autres
Publié: (2026)
Elastic anisotropy in heterogeneous materials
par: Nagaraja, Abhilash M
Publié: (2022)
par: Nagaraja, Abhilash M
Publié: (2022)
Generative design of inorganic materials
par: Recatala-Gomez, Jose, et autres
Publié: (2026)
par: Recatala-Gomez, Jose, et autres
Publié: (2026)
Sustainability-informed materials design
par: Woods-Robinson, Rachel, et autres
Publié: (2026)
par: Woods-Robinson, Rachel, et autres
Publié: (2026)
Clay metaBrick-based motif to enhance thermal and acoustic insulation
par: Lemkalli, Brahim, et autres
Publié: (2023)
par: Lemkalli, Brahim, et autres
Publié: (2023)
Accurate Prediction of Tensorial Spectra Using Equivariant Graph Neural Network
par: Hsu, Ting-Wei, et autres
Publié: (2025)
par: Hsu, Ting-Wei, et autres
Publié: (2025)
Physically recurrent neural network for rate and path-dependent heterogeneous materials in a finite strain framework
par: Maia, M. A., et autres
Publié: (2024)
par: Maia, M. A., et autres
Publié: (2024)
Crystalyse: a multi-tool agent for materials design
par: Nduma, Ryan, et autres
Publié: (2025)
par: Nduma, Ryan, et autres
Publié: (2025)
aflow++: a C++ framework for autonomous materials design
par: Oses, C., et autres
Publié: (2022)
par: Oses, C., et autres
Publié: (2022)
Inverse design for Casimir-Lifshitz force near heterogeneous gapped metal surface
par: Boström, M., et autres
Publié: (2024)
par: Boström, M., et autres
Publié: (2024)
Effect of quenched heterogeneity on creep lifetimes of disordered materials
par: Verano-Espitia, Juan Carlos, et autres
Publié: (2024)
par: Verano-Espitia, Juan Carlos, et autres
Publié: (2024)
Large Language Models for Material Property Predictions: elastic constant tensor prediction and materials design
par: Liu, Siyu, et autres
Publié: (2024)
par: Liu, Siyu, et autres
Publié: (2024)
Accelerated prediction of dielectric functions in solar cell materials with graph neural networks
par: Ginter, Caden, et autres
Publié: (2025)
par: Ginter, Caden, et autres
Publié: (2025)
Embedding material graphs using the electron-ion potential: application to material fracture
par: Tawfik, Sherif Abdulkader, et autres
Publié: (2024)
par: Tawfik, Sherif Abdulkader, et autres
Publié: (2024)
Substrate-aware computational design of two-dimensional materials
par: Mazitov, Arslan, et autres
Publié: (2024)
par: Mazitov, Arslan, et autres
Publié: (2024)
Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials
par: Moon, Minseok, et autres
Publié: (2025)
par: Moon, Minseok, et autres
Publié: (2025)
Optical materials discovery and design with federated databases and machine learning
par: Trinquet, Victor, et autres
Publié: (2024)
par: Trinquet, Victor, et autres
Publié: (2024)
exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials
par: Xia, Weiyi, et autres
Publié: (2025)
par: Xia, Weiyi, et autres
Publié: (2025)
AI-driven materials design: a mini-review
par: Cheng, Mouyang, et autres
Publié: (2025)
par: Cheng, Mouyang, et autres
Publié: (2025)
Effect of Group-V Impurities on the Electronic Properties of Germanium Detectors: An Insight from First-Principles Calculations
par: Aryal, Sandip, et autres
Publié: (2025)
par: Aryal, Sandip, et autres
Publié: (2025)
Stoichiometric and Non-stoichiometric Cesium Potassium Antimonide Photocathodes: Ab-initio Insights into its Properties for Photoemission
par: Aryal, Sandip, et autres
Publié: (2025)
par: Aryal, Sandip, et autres
Publié: (2025)
Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry
par: Ko, Tsz Wai, et autres
Publié: (2025)
par: Ko, Tsz Wai, et autres
Publié: (2025)
Materealize: a multi-agent deliberation system for end-to-end material design and synthesis
par: Kim, Seongmin, et autres
Publié: (2026)
par: Kim, Seongmin, et autres
Publié: (2026)
Structural heterogeneity-induced enhancement of transverse magneto-thermoelectric conversion revealed by thermoelectric imaging in functionally graded materials
par: Park, Sang J., et autres
Publié: (2026)
par: Park, Sang J., et autres
Publié: (2026)
Phase field study of the effective fracture energy increase during dynamic crack propagation in disordered heterogeneous materials
par: Henry, Hervé
Publié: (2025)
par: Henry, Hervé
Publié: (2025)
Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
par: Evans, Matthew L., et autres
Publié: (2024)
par: Evans, Matthew L., et autres
Publié: (2024)
Learning disentangled latent representations facilitates discovery and design of functional materials
par: Cha, Jaehoon, et autres
Publié: (2025)
par: Cha, Jaehoon, et autres
Publié: (2025)
Generative deep learning for the inverse design of materials
par: Long, Teng, et autres
Publié: (2024)
par: Long, Teng, et autres
Publié: (2024)
Documents similaires
-
Symmetry-guided data-driven discovery of native quantum defects in two-dimensional materials
par: Tsai, Jeng-Yuan, et autres
Publié: (2024) -
Bridging deep learning force fields and electronic structures with a physics-informed approach
par: Qi, Yubo, et autres
Publié: (2024) -
Crystal Hypergraph Convolutional Networks
par: Heilman, Alexander J., et autres
Publié: (2024) -
Towards Accurate Prediction of Configurational Disorder Properties in Materials using Graph Neural Networks
par: Fang, Zhenyao, et autres
Publié: (2024) -
Graph Transformer Networks for Accurate Band Structure Prediction: An End-to-End Approach
par: Gong, Weiyi, et autres
Publié: (2024)