Cover Image

Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Makino, Daisuke, Goto, Tatsuya, Suga, Yoshinori
Format:	Preprint
Published:	2025
Subjects:	Materials Science Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Bridging Theory and Experiment in Materials Discovery: Machine-Learning-Assisted Prediction of Synthesizable Structures
by: Xin, Yu, et al.
Published: (2025)

A Materials Map Integrating Experimental and Computational Data via Graph-Based Machine Learning for Enhanced Materials Discovery
by: Hashimoto, Yusuke, et al.
Published: (2025)

Machine Learning-Driven Optimization of TPMS Architected Materials Using Simulated Annealing
by: Mishra, Akshansh
Published: (2024)

Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials - A review
by: Nematov, Dilshod, et al.
Published: (2025)

From Natural Language to Materials Discovery:The Materials Knowledge Navigation Agent
by: Zhuang, Genmao, et al.
Published: (2026)

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

MADE: Benchmark Environments for Closed-Loop Materials Discovery
by: Malik, Shreshth A, et al.
Published: (2026)

Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling
by: Cheng, Mouyang, et al.
Published: (2025)

Pre-training Graph Neural Networks with Structural Fingerprints for Materials Discovery
by: Jia, Shuyi, et al.
Published: (2025)

Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates
by: Okabe, Ryotaro, et al.
Published: (2024)

Extended Low-Rank Approximation Accelerates Learning of Elastic Response in Heterogeneous Materials
by: Karmakar, Prabhat, et al.
Published: (2025)

Rapid Machine Learning-Driven Detection of Pesticides and Dyes Using Raman Spectroscopy
by: Binh, Quach Thi Thai, et al.
Published: (2025)

Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scores
by: Amirian, Benhour, et al.
Published: (2025)

Foundation-Model Surrogates Enable Data-Efficient Active Learning for Materials Discovery
by: Hu, Jeffrey, et al.
Published: (2026)

Machine Learning-Driven Creep Law Discovery Across Alloy Compositional Space
by: Chen, Hongshun, et al.
Published: (2026)

Simulated Annealing-based Candidate Optimization for Batch Acquisition Functions
by: Alvi, Sk Md Ahnaf Akif, et al.
Published: (2026)

Robust Machine Learning Framework for Reliable Discovery of High-Performance Half-Heusler Thermoelectrics
by: Athar, Shoeb, et al.
Published: (2026)

A New Workflow for Materials Discovery Bridging the Gap Between Experimental Databases and Graph Neural Networks
by: Schoener, Brandon, et al.
Published: (2026)

DataScribe: An AI-Native, Policy-Aligned Web Platform for Multi-Objective Materials Design and Discovery
by: Singh, Divyanshu, et al.
Published: (2026)

Toward Multi-Fidelity Machine Learning Force Field for Cathode Materials
by: Dong, Guangyi, et al.
Published: (2025)

Composite Material Design for Optimized Fracture Toughness Using Machine Learning
by: Jahromi, Mohammad Naqizadeh, et al.
Published: (2024)

Role of Large Language Models and Retrieval-Augmented Generation for Accelerating Crystalline Material Discovery: A Systematic Review
by: Oche, Agada Joseph, et al.
Published: (2025)

34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery
by: Zimmermann, Yoel, et al.
Published: (2025)

Efficient and Accurate Spatial Mixing of Machine Learned Interatomic Potentials for Materials Science
by: Birks, Fraser, et al.
Published: (2025)

Advancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment prediction
by: Verma, Apoorv, et al.
Published: (2025)

Mixed-Variable Global Sensitivity Analysis For Knowledge Discovery And Efficient Combinatorial Materials Design
by: Comlek, Yigitcan, et al.
Published: (2023)

Semantic Embeddings of Chemical Elements for Enhanced Materials Inference and Discovery
by: Jia, Yunze, et al.
Published: (2025)

Estimation of Electronic Band Gap Energy From Material Properties Using Machine Learning
by: Barad, Sagar Prakash, et al.
Published: (2024)

Machine Learning Based Prediction of Proton Conductivity in Metal-Organic Frameworks
by: Han, Seunghee, et al.
Published: (2024)

Discovery of 2D Materials via Symmetry-Constrained Diffusion Model
by: Xu, Shihang, et al.
Published: (2024)

Crystal Systems Classification of Phosphate-Based Cathode Materials Using Machine Learning for Lithium-Ion Battery
by: Yadav, Yogesh, et al.
Published: (2025)

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

LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discovery
by: Boyar, Onur, et al.
Published: (2025)

PriM: Principle-Inspired Material Discovery through Multi-Agent Collaboration
by: Lai, Zheyuan, et al.
Published: (2025)

Self-Optimizing Machine Learning Potential Assisted Automated Workflow for Highly Efficient Complex Systems Material Design
by: Li, Jiaxiang, et al.
Published: (2025)

Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations
by: Cangi, Attila, et al.
Published: (2024)

Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning
by: Kang, Kisung, et al.
Published: (2024)

Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritization
by: Schwarting, Marcus, et al.
Published: (2025)

Real-time Multi-instrument Autonomous Discovery of Novel Phase-change Memory Materials
by: Lee, Chih-Yu, et al.
Published: (2026)

Are LLMs Ready for Real-World Materials Discovery?
by: Miret, Santiago, et al.
Published: (2024)