:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Yamamoto, Ryoma, Takahashi, Akira, Terayama, Kei, Kumagai, Yu, Oba, Fumiyasu
Format:	Preprint
Published:	2026
Subjects:	Materials Science
Online Access:	https://arxiv.org/abs/2603.26060
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Deep Learning-Based Extraction of Promising Material Groups and Common Features from High-Dimensional Data: A Case of Optical Spectra of Inorganic Crystals
by: Takahashi, Akira, et al.
Published: (2025)

Update of PHYSBO: Improving Usability and Portability of Bayesian Optimization for Physics and Materials Research
by: Motoyama, Yuichi, et al.
Published: (2026)

Machine Learning Approaches to Point Defects in Non-Metallic Materials: A Review of Methods
by: Kumagai, Yu, et al.
Published: (2026)

Charting the Landscape of Oxygen Ion Conductors: A 60-Year Dataset with Interpretable Regression Models
by: Jang, Seong-Hoon, et al.
Published: (2026)

Physics-Based Factorized Machine Learning for Predicting Ionic Dielectric Tensors
by: Takigawa, Atsushi, et al.
Published: (2025)

Atomic-scale phase-field modeling with universal machine learning potentials
by: Masuda, Kairi, et al.
Published: (2025)

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

Data-driven study of the enthalpy of mixing in the liquid phase
by: Deffrennes, Guillaume, et al.
Published: (2024)

Machine Learning Materials Properties with Accurate Predictions, Uncertainty Estimates, Domain Guidance, and Persistent Online Accessibility
by: Jacobs, Ryan, et al.
Published: (2024)

On-demand phase-field modeling: Three-dimensional Landau energy for HfO2 through machine learning
by: Tamura, Yusuke, et al.
Published: (2025)

Investigation of Hole Dopability in Oxygen $2p$-Dominated Bands
by: Vu, Thi Ngoc Huyen, et al.
Published: (2025)

LLM-guided phase diagram construction through high-throughput experimentation
by: Tamura, Ryo, et al.
Published: (2026)

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

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

Atomic networks as highways for holes in oxygen-deficient amorphous oxides
by: Costa-Amaral, Rafael, et al.
Published: (2025)

Machine Learning Prediction of Charged Defect Formation Energies from Crystal Structures
by: Kiyohara, Shin, et al.
Published: (2025)

Sampling Latent Material-Property Information From LLM-Derived Embedding Representations
by: Gilligan, Luke P. J., et al.
Published: (2024)

ZEBRA: Zero-Shot Example-Based Retrieval Augmentation for Commonsense Question Answering
by: Molfese, Francesco Maria, et al.
Published: (2024)

Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural Networks
by: Li, Longze, et al.
Published: (2023)

Native defects and $p$-type dopability in transparent $β$-TeO$_2$: A first-principles study
by: Huyen, Vu Thi Ngoc, et al.
Published: (2024)

Embedded Nonlocal Operator Regression (ENOR): Quantifying model error in learning nonlocal operators
by: Fan, Yiming, et al.
Published: (2024)

Rapid and Scalable Synthesis of Alkali Metal-Intercalated C$_{60}$ Superconductors
by: Iyo, Akira, et al.
Published: (2025)

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

A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials
by: Fang, Zhenyao, et al.
Published: (2025)

Inverse Design of Amorphous Materials with Targeted Properties
by: Finkler, Jonas A., et al.
Published: (2025)

Iterative Corpus Refinement for Materials Property Prediction Based on Scientific Texts
by: Zhang, Lei, et al.
Published: (2025)

Thermophysical properties of spark plasma sintered UCo: a comparison with machine learning predictions
by: Sun, Yifan, et al.
Published: (2026)

Entropy-based Active Learning of Graph Neural Network Surrogate Models for Materials Properties
by: Allotey, Johannes, et al.
Published: (2021)

Applications of Machine Learning in Polymer Materials: Property Prediction, Material Design, and Systematic Processes
by: Li, Hongtao Guo Shuai Li Shu
Published: (2025)

A New Spin on Material Properties: Local Magnetic Structure in Functional and Quantum Materials
by: Frandsen, Benjamin A., et al.
Published: (2024)

Material Microstructure Design Using VAE-Regression with Multimodal Prior
by: Sardeshmukh, Avadhut, et al.
Published: (2024)

Material Hardness Descriptor Derived by Symbolic Regression
by: Tantardini, Christian, et al.
Published: (2023)

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials
by: Makino, Daisuke, et al.
Published: (2025)

Kolmogorov-Arnold Networks Applied to Materials Property Prediction
by: Jacobs, Ryan, et al.
Published: (2026)

Magnetic Properties of Potential Li-ion Battery Materials
by: Akanda, Md Rakibul Karim, et al.
Published: (2025)

Composition-Weighted Symbolic Regression for General-Purpose Property Prediction
by: Huang, Yang, et al.
Published: (2026)

Exploring Intrinsic and Extrinsic $p$-type Dopability of Atomically Thin $β$-TeO$_2$ from First Principles
by: Costa-Amaral, Rafael, et al.
Published: (2024)

Structure-Property Relationship in Disordered Hyperuniform Materials: Microstructure Representation, Field Fluctuations and Effective Properties
by: Zhong, Liyu, et al.
Published: (2025)

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

Machine Learning for Electrode Materials: Property Prediction via Composition
by: Wu, Hao, et al.
Published: (2026)