Materials Property Prediction

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Overview

Materials property prediction studies how composition, atomic structure, microstructure, and processing determine properties such as strength, stability, conductivity, and band gap. Common approaches include descriptor-based regression, graph and equivariant neural networks, pretrained representations, and physics-informed or simulation-based surrogates; multimodal methods also combine structures with images, spectra, or experimental records. These models can speed screening and guide design when direct experiments or first-principles calculations are costly, but performance often depends on having enough relevant, reliable labels. A central challenge is generalizing beyond familiar datasets and computational settings to new materials and experiments, while providing calibrated uncertainty and physically meaningful explanations.

Latest papers

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175 papers21 papers in the last four weeks, up 24% on the four weeks before. 0.1% of all new papers.

  1. GlazyBench: A Benchmark for Ceramic Glaze Property Prediction and Image Generation

    May 7, 2026Ziyu Zhai, Siyou Li, Juexi Shao +1Conditional Image GenerationMaterials Property Prediction

    Queen Mary University of London

  2. Polarizable atomic multipoles for learning long-range electrostatics

    May 7, 2026Yoonjae Park, Dongjin Kim, Daniel S. King +5Machine Learning Interatomic PotentialsMaterials Property Prediction

    Department of Chemistry, UC Berkeley, California 94720, United States · Bakar Institute of Digital Materials for the Planet, UC Berkeley, California 94720, United States · Lawrence Livermore National Laboratory, Livermore, CA, USA +1

  3. Diversity-Aware Active Learning Builds Materials Datasets for Current and Future Objectives

    May 6, 2026Rafael Espinosa Castañeda, Ashley Dale, Hongchen Wang +6Diversity-Aware SamplingData Selection

    Department of Materials Science and Engineering, University of Toronto, Canada. · Vector Institute for Artificial Intelligence, Toronto, Canada · Department of Computer Science, Princeton University +2

  4. Data-Efficient Indentation Size Effect Mitigation in Steels Using Machine Learning and a Physics-Constrained Neural Network

    Apr 30, 2026Radmir Karamov, Tagir KaramovMaterials Property PredictionMaterials Science

    Center for Material Technologies, Skolkovo Institute of Science and Technology · Center for Petroleum Science and Engineering, Skolkovo Institute of Science and Technology

  5. Benchmarking bandgap prediction in semiconductors under experimental and realistic evaluation settings

    Apr 28, 2026Haolin Wang, Xianyuan Liu, Anna Jungbluth +3Benchmark DesignInterpretable ML

    Centre for Machine Intelligence, University of Sheffield, Sheffield, S1 3JD, UK · School of Computer Science, University of Sheffield, Sheffield, S1 4DP, UK · Climate Office, European Space Agency, Harwell, OX11 0FD, UK +2

  6. Generalizable Friction Coefficient Estimation via Material Embedding and Proxy Interaction Modeling

    Apr 27, 2026Zhendong Wang, Huamin WangSurrogate ModelingMaterials Property Prediction

    Style3D Research Hangzhou, China

  7. Agentic Fusion of Large Atomic and Language Models to Accelerate Superconductor Discovery

    Apr 26, 2026Mingze Li, Yu Rong, Songyou Li +16AI Agents for Scientific DiscoveryMaterials Property Prediction

    Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China. · DAMO Academy, Alibaba Group, Hangzhou, China. · Hupan Lab, Hangzhou, China. +2

  8. Crystal Fractional Graph Neural Network for Energy Prediction of High-Entropy Alloys

    Apr 26, 2026Takanori Kotama, Yang HuangGraph Neural NetworksMaterials Property Prediction

    School of Informatics, Nagoya University, Nagoya 4648601, Japan · University of Science and Technology of China, Hefei 230026, China · Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215213, China

  9. Multimodal Transformer for Sample-Aware Prediction of Metal-Organic Framework Properties

    Apr 21, 2026Seunghee Han, Jaewoong Lee, Jihan KimMultimodal PretrainingMaterials Property Prediction

    Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea. · Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.

  10. Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification

    Apr 15, 2026Mohammad Nooraiepour, Zezhang Song, Wei Li +1Monte Carlo DropoutPhysics-Informed ML

    Environmental Geosciences, Department of Geosciences, University of Oslo, P.O. Box 1047 Blindern, 0316 Oslo, Norway. · National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), China · College of Geosciences, China University of Petroleum (Beijing), China +3

  11. XANE(3): An E(3)-Equivariant Graph Neural Network for Accurate Prediction of XANES Spectra from Atomic Structures

    Apr 13, 2026Vitor F. Grizzi, Luke N. Pretzie, Jiayi Xu +1Materials Property PredictionEquivariant GNNs

    Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, IL 60439, USA

  12. VLMaterial: Vision-Language Model-Based Camera-Radar Fusion for Physics-Grounded Material Identification

    Apr 13, 2026Jiangyou Zhu, He ChenCamera-Radar FusionMaterials Property Prediction

    Department of Inforation Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China

  13. A collaborative agent with two lightweight synergistic models for autonomous crystal materials research

    Apr 13, 2026Tongyu Shi, Yutang Li, Zhanyuan Li +9Autonomous Research AgentsMulti-Agent LLM Systems

    Materials Artificial Intelligence Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China · University of Chinese Academy of Sciences, Beijing 100049, China · Institute for Advanced Study, Chengdu University, Chengdu 610106, Sichuan, China. +1

  14. ReadMOF: Structure-Free Semantic Embeddings from Systematic MOF Nomenclature for Machine Learning

    Apr 12, 2026Kewei Zhu, Cameron Wilson, Bartosz Mazur +3Molecular Representation LearningMaterials Science

    Department of Chemical Engineering, University College London, London WC1E 7JE, United Kingdom · Department of Micro, Nano, and Bioprocess Engineering, Faculty of Chemistry, Wroclaw University of Science and Technology, Wroclaw 50-370, Poland · Manufacturing Futures Lab, University College London, 7 Sidings Street, London, E20 2AE, United Kingdom

  15. Continuous PT-Symmetry Breaking as a Design Variable for Giant Altermagnetic Spin Splitting

    Apr 11, 2026Kichan Chun, Gunn KimMaterials ScienceSymmetry Breaking

    Department of Physics, Sejong University, Seoul 05006, Korea

  16. SemiConLens: Visual Analytics for 2D Semiconductor Discovery

    Apr 11, 2026Kavinda Athapaththu, Shiwei Chen, Yuan Fang +3Materials ScienceVisual Analytics

    Nanyang Technological University · Singapore Management University · Singapore University of Technology and Design

  17. Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials

    Apr 9, 2026R. Seaton Ullberg, Megan C. Davis, Jeremy N. Schroeder +5Materials Property PredictionMaterials Science

    Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA · Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79409, USA · Weapon Stockpile Modernization Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA

  18. Interpretation of Crystal Energy Landscapes with Kolmogorov-Arnold Networks

    Apr 6, 2026Gen Zu, Ning Mao, Claudia Felser +1Materials Property PredictionInterpretable ML

    Max Planck Institute for Chemical Physics of Solids, 01187, Dresden, Germany · Department of Physics and Astronomy, University of Tennessee, Knoxville, TN 37996, USA · Min H. Kao Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee 37996, USA

  19. PolyJarvis: An LLM-Orchestrated Agent for Automated All-Atom Molecular Dynamics of Amorphous Homopolymers

    Apr 2, 2026Alexander Zhao, Achuth Chandrasekhar, Amir Barati FarimaniLLM AgentsMaterials Science

    Department of Materials Science and Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA · Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA

  20. Physics-Informed Neural Networks for Predicting Hydrogen Sorption in Geological Formations: Thermodynamically Constrained Deep Learning Integrating Classical Adsorption Theory

    Mar 30, 2026Mohammad Nooraiepour, Mohammad Masoudi, Zezhang Song +1Materials Property PredictionPhysics-Informed ML

    Faculty of Mathematics & Natural Sciences, University of Oslo, P.O. Box 1047 Blindern, 0316 Oslo, Norway · SINTEF Industry, Applied Geoscience Department, 7465 Trondheim, Norway · College of Geosciences, China University of Petroleum, Beijing 102249, China

  21. Data-Driven Estimation of the interfacial Dzyaloshinskii-Moriya Interaction with Machine Learning

    Mar 28, 2026Davi Rodrigues, Andrea Meo, Ali Hasan +8Materials ScienceMaterials Property Prediction

    Department of Electrical and Information Engineering, Politecnico di Bari, 70126 Bari, Italy · Department of Mathematical and Computer Sciences, Physical Sciences and Earth Sciences, University of Messina, 98166, Messina, Italy · Istituto Nazionale di Ricerca Metrologica, 10135, Torino, Italy +1

  22. SLAT-Phys: Fast Material Property Field Prediction from Structured 3D Latents

    Mar 25, 2026Rocktim Jyoti Das, Dinesh ManochaMaterials Property Prediction

    University of Maryland, College Park

  23. Multitask-Informed Prior for In-Context Learning on Tabular Data: Application to Steel Property Prediction

    Mar 24, 2026Dimitrios Sinodinos, Bahareh Nikpour, Jack Yi Wei +5Multi-Task LearningTabular Foundation Models

    Dep. of Electrical and Computer Engineering, McGill University · Mila – Québec AI Institute · Dep. of Mining and Materials Engineering, McGill University +1

  24. BOxCrete: A Bayesian Optimization Open-Source AI Model for Concrete Strength Forecasting and Mix Optimization

    Mar 23, 2026Bayezid Baten, M. Ayyan Iqbal, Sebastian Ament +2Materials Property Prediction

    Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA · Meta, Menlo Park, California, USA.

  25. TabPFN Extensions for Interpretable Geotechnical Modelling

    Mar 22, 2026Taiga Saito, Yu Otake, Daijiro Mizutani +1Uncertainty QuantificationTabular Foundation Models

    Department of Civil and Environmental Engineering, Tohoku University, 6-6-06 Aramaki Aoba, Aoba-ku, Sendai, Miyagi 980-8579, Japan · Research Organization of Information and Systems, The Institute of Statistical Mathematics 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan · Department of Statistical Science, The Graduate University for Advanced Studies 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan

  26. Anisotropic Permeability Tensor Prediction from Porous Media Microstructure via Physics-Informed Progressive Transfer Learning with Hybrid CNN-Transformer

    Mar 18, 2026Mohammad NooraiepourMaterials Property PredictionPorous Media Flow

    Faculty of Mathematics and Natural Sciences, University of Oslo, PO Box 1047 Blindern, 0316 Oslo, Norway

  27. Self-Conditioned Denoising for Atomistic Representation Learning

    Mar 17, 2026Tynan Perez, Rafael Gomez-BombarelliSelf-Supervised LearningMolecular Representation Learning

    Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, MA, USA · Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, MA, USA

  28. Benchmarking Machine Learning Approaches for Polarization Mapping in Ferroelectrics Using 4D-STEM

    Mar 16, 2026Matej Martinc, Goran Dražič, Anton Kokalj +5Sim-to-Real TransferMaterials Science

    Jožef Stefan Institute, Jamova 39, Ljubljana, Slovenia

  29. Predicting Stress-strain Behaviors of Additively Manufactured Materials via Loss-based and Activation-based Physics-informed Machine Learning

    Mar 15, 2026Chenglong Duan, Dazhong WuMaterials Property PredictionPhysics-Informed ML

    Department of Mechanical and Aerospace Engineering, College of Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA

  30. Intelligent Materials Modelling: Large Language Models Versus Partial Least Squares Regression for Predicting Polysulfone Membrane Mechanical Performance

    Mar 14, 2026Dingding Cao, Mieow Kee Chan, Wan Sieng Yeo +2Materials Property PredictionPolymer Property Prediction

    School of Big Data, Baoshan University, Baoshan, 678000, Yunnan Province, China. · Centre for Water Research, Faculty of Engineering, Built Environment and Information Technology, SEGi University. Jalan Teknologi, Kota Damansara, 47810 Petaling Jaya, Selangor Darul Ehsan, Malaysia. · School of Energy and Chemical Engineering, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900 Sepang, Selangor, Malaysia. +3