Copertina

Scale-Dependent Input Representation and Confidence Estimation for LLMs in Materials Property Prediction

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ozawa, Shuichiro, Takahara, Izumi, Mizoguchi, Teruyasu
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Materials Science
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Accelerated Inorganic Materials Design with Generative AI Agents
di: Takahara, Izumi, et al.
Pubblicazione: (2025)

Inverse Materials Design via Joint Generation of Crystal Structures and Local Electronic Descriptors
di: Okuda, Ibuki, et al.
Pubblicazione: (2026)

Crystal orbital overlap population based on all-electron ab initio simulation with numeric atom-centered orbitals and its application to chemical-bonding analysis in Li-intercalated layered materials
di: Takahara, Izumi, et al.
Pubblicazione: (2024)

Generative Inverse Design of Crystal Structures via Diffusion Models with Transformers
di: Takahara, Izumi, et al.
Pubblicazione: (2024)

Generative Inverse Estimation of 3D Atomic Coordination from Near-Edge Spectra via Equivariant Diffusion Models
di: Okubo, Ren, et al.
Pubblicazione: (2026)

Achieving Robust Extrapolation in Materials Property Prediction via Decoupled Transfer Learning
di: Sugiura, Tasuku, et al.
Pubblicazione: (2026)

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties with Phonon-Informed Datasets
di: Benítez, Pol, et al.
Pubblicazione: (2025)

Long-range interaction effects on the phase transition, mechanical effect, and electric field response of BaTiO3 by machine learning potentials
di: Chen, Po-Yen, et al.
Pubblicazione: (2026)

Transition from Homogeneous to Domain-Wall-Mediated Polarization Switching in BaTiO3: A Machine-Learning Molecular Dynamics Study
di: Chen, Po-Yen, et al.
Pubblicazione: (2026)

Electric Field-Induced Phase Transitions and Hysteresis in Ferroelectric HfO2 Captured with Machine Learning Potential
di: Chen, Po-Yen, et al.
Pubblicazione: (2025)

Effect of uniaxial compressive stress on polarization switching and domain wall formation in tetragonal phase BaTiO3 via machine learning potential
di: Chen, Po-Yen, et al.
Pubblicazione: (2026)

Twist-Angle Engineering of Moiré Potentials for High-Performance Ionics in Bilayer Graphene
di: Fukuzawa, Gen, et al.
Pubblicazione: (2026)

Finite-size effects and energy alignment in molecular XANES under periodic boundary conditions: A systematic comparison of core-hole treatments
di: Fujikata, Yu, et al.
Pubblicazione: (2026)

Ultrafast Sliding Ferroelectric Switching in Bilayer Hexagonal Boron Nitride Revealed by Deep Learning Molecular Dynamics
di: Wang, Yinan, et al.
Pubblicazione: (2026)

Origin of Reduced Coercive Field in ScAlN: Synergy of Structural Softening and Dynamic Atomic Correlations
di: Sahashi, Ryotaro, et al.
Pubblicazione: (2026)

Decoupling structural and bonding effects on ferroelectric switching in ScAlN via molecular dynamics under an applied electric field
di: Sahashi, Ryotaro, et al.
Pubblicazione: (2026)

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

Towards Agentic Intelligence for Materials Science
di: Zhang, Huan, et al.
Pubblicazione: (2026)

Physics Aware Representation Learning on Electronic Charge Density for Materials Property Prediction
di: Kumar, Kammampati Sai, et al.
Pubblicazione: (2026)

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

Defect‐Driven Reconstruction of Bismuth Nanoflowers via Precursor Engineering for Highly Efficient CO2‐to‐Formate Electrochemical Reduction
di: Jiaying Yan, et al.
Pubblicazione: (2025)

Defect‐Driven Reconstruction of Bismuth Nanoflowers via Precursor Engineering for Highly Efficient CO2‐to‐Formate Electrochemical Reduction
di: Jiaying Yan, et al.
Pubblicazione: (2025)

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

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

Material Property Prediction with Element Attribute Knowledge Graphs and Multimodal Representation Learning
di: Huang, Chao, et al.
Pubblicazione: (2024)

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

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

On equivalence of two formulas of orbital magnetic susceptibility for tight-binding models
di: Mizoguchi, Tomonari, et al.
Pubblicazione: (2024)

In context learning Foundation models for Materials Property Prediction with Small datasets
di: Li, Qinyang, et al.
Pubblicazione: (2025)

Bayesian Parameter Estimation for Predictive Modeling of Illumination-Dependent Current-Voltage Curves
di: Kim, Eunchi, et al.
Pubblicazione: (2026)

Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions
di: Wang, Hongchen, et al.
Pubblicazione: (2024)

The Predictive Power of Chemical Bonding Analysis in Materials: a Perspective on Optoelectronic Properties
di: Saleh, Gabriele, et al.
Pubblicazione: (2026)

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

Physical Encoding Improves OOD Performance in Deep Learning Materials Property Prediction
di: Fu, Nihang, et al.
Pubblicazione: (2024)

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

Supervised Pretraining for Material Property Prediction
di: Rahman, Chowdhury Mohammad Abid, et al.
Pubblicazione: (2025)

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

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

Descriptor and Graph-based Molecular Representations in Prediction of Copolymer Properties Using Machine Learning
di: Kazemi-Khasragh, Elaheh, et al.
Pubblicazione: (2025)

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