Imagen de Portada

Exact Expansion Formalism for Transport Properties of Heterogeneous Materials Characterized by Arbitrary Continuous Random Fields

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhong, Liyu, Mao, Sheng
Formato:	Preprint
Publicado:	2025
Materias:	Materials Science
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Ultra-Efficient Reconstruction of Anisotropic Hyperuniform Continuous Random Fields in 2D and 3D via Generalized Spectral Filtering
por: Zhong, Liyu, et al.
Publicado: (2025)

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

Cylindrical Cavity Expansion: A Novel Method for Characterizing the Mechanical Properties of Soft Materials
por: Li, Jian, et al.
Publicado: (2024)

Material-Property-Field-based Deep Neural Network in Hopfield Framework
por: Hu, Yanxiao, et al.
Publicado: (2026)

Database of Tensorial Optical and Transport Properties of Materials From the Wannier Function Method
por: Fang, Zhenyao, et al.
Publicado: (2025)

Influence of Built-in Electric Fields on the Optoelectronic and Catalytic Properties of Two-Dimensional Materials
por: Kong, Kai, et al.
Publicado: (2025)

Control of Mechanical and Fracture Properties in Two-phase Materials Reinforced by Continuous, Irregular Networks
por: Magrini, Tommaso, et al.
Publicado: (2023)

Magnetic-Field and Strain Engineering of Modulated Transverse Transport in Altermagnetic Topological Materials
por: Yang, Xiuxian, et al.
Publicado: (2025)

Electric Field Modulation of Spin Transport
por: Zucchetti, Carlo, et al.
Publicado: (2021)

Experimental Characterizing Mechanical and Physical Properties of Water Lily Polyester Matrix Composite Materials
por: Melese Shiferaw Kebede, et al.
Publicado: (2024)

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties
por: Weber, John L., et al.
Publicado: (2025)

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties
por: Wines, Daniel, et al.
Publicado: (2024)

Bridging Crystal Structure and Material Properties via Bond-Centric Descriptors
por: Zhang, Jian-Feng, et al.
Publicado: (2026)

Single Crystal Growth and Transport Properties of van der Waals Materials $AB$Te$_\mathbf{4}$ ($A/B$ = Ti, Zr, Hf)
por: Hasuo, Yuto, et al.
Publicado: (2024)

Three-Dimensional Construction of Hyperuniform, Nonhyperuniform and Antihyperuniform Random Media via Spectral Density Functions and Their Transport Properties
por: Shi, Wenlong, et al.
Publicado: (2024)

A Guide to Fully Characterize the Fracture Properties of Cementitious Materials from Simple Experiments
por: Saha, Subhrangsu, et al.
Publicado: (2026)

Non-equilibrium Ion Transport in a Hybrid Battery Material
por: Cattermull, J., et al.
Publicado: (2025)

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

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

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

Novel Heusler Materials for Spintronic Applications: Growth, Characterizations and Applications
por: Kumar, Ravinder, et al.
Publicado: (2025)

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

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

Chiral Decomposition of Twisted Graphene Multilayers with Arbitrary Stacking
por: Zhang, ShengNan, et al.
Publicado: (2020)

Equivariant Neural Network Force Fields for Magnetic Materials
por: Yuan, Zilong, et al.
Publicado: (2024)

Dynamics of Transition Metal Ion Transport in High-Gradient Magnetic Fields
por: Benhal, Prateek, et al.
Publicado: (2025)

Recent Breakthrough in AI-Driven Materials Science: Tech Giants Introduce Groundbreaking Models
por: Liu, Miao, et al.
Publicado: (2024)

Orchestration of Heterogeneous Experimental Machines via ROS2 for Automated Bulk Intermetallic Synthesis
por: Wang, Wei-Sheng, et al.
Publicado: (2025)

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

Deciphering Chemical Ordering in High Entropy Materials: A Machine Learning-Accelerated High-throughput Cluster Expansion Approach
por: Vazquez, Guillermo, et al.
Publicado: (2024)

Calculating Magnetic Properties of Two-Dimensional Materials for Memory Applications
por: Akanda, Md Rakibul Karim, et al.
Publicado: (2024)

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

Unveiling the Core of Materials Properties via SISSO and Sensitivity Analysis
por: Foppa, Lucas, et al.
Publicado: (2026)

Integrating 2D Magnets for Quantum Devices: from Materials and Characterization to Future Technology
por: Zhong, Han, et al.
Publicado: (2024)

Mechanism of Charge Transport in Mixed-Valence 2D Layered Hybrid Bronze Materials
por: Akter, Suchona, et al.
Publicado: (2025)

Modern Solid Electrolytes for All-Solid-State Batteries: Materials Chemistry, Structure, and Transport
por: Butenko, Denys, et al.
Publicado: (2026)

Microstructural and Transport Characteristics of Triply Periodic Bicontinuous Materials
por: Torquato, Salvatore, et al.
Publicado: (2024)

Device-Scale Atomistic Simulations of Heat Transport in Advanced Field-Effect Transistors
por: Xu, Ke, et al.
Publicado: (2025)

Random Combinatorial Libraries and Automated Nanoindentation for High-Throughput Structural Materials Discovery
por: Chawla, Vivek, et al.
Publicado: (2025)

Accelerating Cathode Material Discovery through Ab Initio Random Structure Searching
por: Zhu, Bonan, et al.
Publicado: (2021)