Saved in:
| Main Authors: | Hu, Jeffrey, Liu, David, Fu, Nihang, Dong, Rongzhi |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2308.02937 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study
by: Omee, Sadman Sadeed, et al.
Published: (2024)
by: Omee, Sadman Sadeed, et al.
Published: (2024)
Physics guided dual Self-supervised learning for structure-based materials property prediction
by: Fu, Nihang, et al.
Published: (2024)
by: Fu, Nihang, et al.
Published: (2024)
TCSP 2.0: Template Based Crystal Structure Prediction with Improved Oxidation State Prediction and Chemistry Heuristics
by: Wei, Lai, et al.
Published: (2025)
by: Wei, Lai, et al.
Published: (2025)
Physical Encoding Improves OOD Performance in Deep Learning Materials Property Prediction
by: Fu, Nihang, et al.
Published: (2024)
by: Fu, Nihang, et al.
Published: (2024)
AlphaCrystal-II: Distance matrix based crystal structure prediction using deep learning
by: Song, Yuqi, et al.
Published: (2024)
by: Song, Yuqi, et al.
Published: (2024)
Data-Driven Topological Analysis of Polymorphic Crystal Structures
by: Dey, Sourin, et al.
Published: (2025)
by: Dey, Sourin, et al.
Published: (2025)
A foundation machine learning potential with polarizable long-range interactions for materials modelling
by: Gao, Rongzhi, et al.
Published: (2024)
by: Gao, Rongzhi, et al.
Published: (2024)
Facet: highly efficient E(3)-equivariant networks for interatomic potentials
by: Miklaucic, Nicholas, et al.
Published: (2025)
by: Miklaucic, Nicholas, et al.
Published: (2025)
CSPBench: a benchmark and critical evaluation of Crystal Structure Prediction
by: Wei, Lai, et al.
Published: (2024)
by: Wei, Lai, et al.
Published: (2024)
Out-of-distribution materials property prediction using adversarial learning based fine-tuning
by: Li, Qinyang, et al.
Published: (2024)
by: Li, Qinyang, et al.
Published: (2024)
Foundation-Model Surrogates Enable Data-Efficient Active Learning for Materials Discovery
by: Hu, Jeffrey, et al.
Published: (2026)
by: Hu, Jeffrey, et al.
Published: (2026)
Low dimensional fragment-based descriptors for property predictions in inorganic materials with machine learning
by: Islam, Md Mohaiminul
Published: (2024)
by: Islam, Md Mohaiminul
Published: (2024)
In context learning Foundation models for Materials Property Prediction with Small datasets
by: Li, Qinyang, et al.
Published: (2025)
by: Li, Qinyang, et al.
Published: (2025)
Accurate and efficient predictions of keyhole dynamics in laser materials processing using machine learning-aided simulations
by: Zhang, Jiahui, et al.
Published: (2024)
by: Zhang, Jiahui, et al.
Published: (2024)
Accurate prediction of structural and mechanical properties on amorphous materials enabled through machine-learning potentials: a case study of silicon nitride
by: Nayak, Ganesh Kumar, et al.
Published: (2024)
by: Nayak, Ganesh Kumar, et al.
Published: (2024)
Environment-adaptive machine learning potentials
by: Nguyen, Ngoc Cuong, et al.
Published: (2024)
by: Nguyen, Ngoc Cuong, et al.
Published: (2024)
Feature-based prediction of properties of cross-linked epoxy polymers by molecular dynamics and machine learning techniques
by: S., Sindu B., et al.
Published: (2023)
by: S., Sindu B., et al.
Published: (2023)
Accelerating material melting temperature predictions by implementing machine learning potentials in the SLUSCHI package
by: CampBell, Audrey, et al.
Published: (2024)
by: CampBell, Audrey, et al.
Published: (2024)
Thermophysical properties of spark plasma sintered UCo: a comparison with machine learning predictions
by: Sun, Yifan, et al.
Published: (2026)
by: Sun, Yifan, et al.
Published: (2026)
Screening of material defects using universal machine-learning interatomic potentials
by: Berger, Ethan, et al.
Published: (2025)
by: Berger, Ethan, et al.
Published: (2025)
Computational toolkit for predicting thickness of 2D materials using machine learning and autogenerated dataset by large language model
by: Ekuma, Chinedu
Published: (2024)
by: Ekuma, Chinedu
Published: (2024)
Probing out-of-distribution generalization in machine learning for materials
by: Li, Kangming, et al.
Published: (2024)
by: Li, Kangming, et al.
Published: (2024)
Shotgun crystal structure prediction using machine-learned formation energies
by: Liu, Chang, et al.
Published: (2023)
by: Liu, Chang, et al.
Published: (2023)
Electron transport properties of heterogeneous interfaces in solid electrolyte interphase on lithium metal anodes
by: Zhou, Xiangyi, et al.
Published: (2025)
by: Zhou, Xiangyi, et al.
Published: (2025)
Composition based machine learning to predict phases & strength of refractory high entropy alloys
by: Iyengar, M. Sreenidhi, et al.
Published: (2025)
by: Iyengar, M. Sreenidhi, et al.
Published: (2025)
Optical materials discovery and design with federated databases and machine learning
by: Trinquet, Victor, et al.
Published: (2024)
by: Trinquet, Victor, et al.
Published: (2024)
Autonomous materials search using machine learning and ab initio calculations for L10-FePt-based quaternary alloys
by: Iwasaki, Yuma, et al.
Published: (2024)
by: Iwasaki, Yuma, et al.
Published: (2024)
Dynamically training machine-learning-based force fields for strongly anharmonic materials
by: Callsen, Martin, et al.
Published: (2026)
by: Callsen, Martin, et al.
Published: (2026)
Cross-scale covariance for material property prediction
by: Jasperson, Benjamin A., et al.
Published: (2024)
by: Jasperson, Benjamin A., et al.
Published: (2024)
Data-efficient machine-learning of complex Fe-Mo intermetallics using domain knowledge of chemistry and crystallography
by: Forti, Mariano, et al.
Published: (2025)
by: Forti, Mariano, et al.
Published: (2025)
Multimodal machine learning with large language embedding model for polymer property prediction
by: Zhang, Tianren, et al.
Published: (2025)
by: Zhang, Tianren, et al.
Published: (2025)
Efficient molecular dynamics simulation of 2D penta-silicene materials using machine learning potentials
by: Nghia, Le Huu, et al.
Published: (2026)
by: Nghia, Le Huu, et al.
Published: (2026)
Heat transport in superionic materials via machine-learned molecular dynamics
by: Zhou, Wenjiang, et al.
Published: (2025)
by: Zhou, Wenjiang, et al.
Published: (2025)
Benchmarking bandgap prediction in semiconductors under experimental and realistic evaluation settings
by: Wang, Haolin, et al.
Published: (2026)
by: Wang, Haolin, et al.
Published: (2026)
Hydrogen permeability prediction in palladium alloys and virtual screening of B2-phase stabilized Pd(100-x-y)CuxMy ternary alloys using machine learning
by: Kolor, Eric, et al.
Published: (2025)
by: Kolor, Eric, et al.
Published: (2025)
Benchmarking empirical and machine-learned interatomic potentials using phase diagram predictions for Lead
by: Hellyar, Tom, et al.
Published: (2026)
by: Hellyar, Tom, et al.
Published: (2026)
Discovery of sustainable energy materials via the machine-learned material space
by: Grunert, Malte, et al.
Published: (2025)
by: Grunert, Malte, et al.
Published: (2025)
Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials
by: Fan, Zheyong, et al.
Published: (2023)
by: Fan, Zheyong, et al.
Published: (2023)
Interpretable machine learned predictions of adsorption energies at the metal--oxide interface
by: Nielsen, Marius Juul, et al.
Published: (2025)
by: Nielsen, Marius Juul, et al.
Published: (2025)
Efficient first principles based modeling via machine learning: from simple representations to high entropy materials
by: Li, Kangming, et al.
Published: (2024)
by: Li, Kangming, et al.
Published: (2024)
Similar Items
-
Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study
by: Omee, Sadman Sadeed, et al.
Published: (2024) -
Physics guided dual Self-supervised learning for structure-based materials property prediction
by: Fu, Nihang, et al.
Published: (2024) -
TCSP 2.0: Template Based Crystal Structure Prediction with Improved Oxidation State Prediction and Chemistry Heuristics
by: Wei, Lai, et al.
Published: (2025) -
Physical Encoding Improves OOD Performance in Deep Learning Materials Property Prediction
by: Fu, Nihang, et al.
Published: (2024) -
AlphaCrystal-II: Distance matrix based crystal structure prediction using deep learning
by: Song, Yuqi, et al.
Published: (2024)