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

  1. Distilling Large-Scale Machine Learning Interatomic Potentials to Predict Long-Timescale Electrolyte Transport Properties

    Oct 29, 2026Ishan Amin, Yue Jian, Nitesh Kumar +10Machine Learning Interatomic PotentialsMaterials Property Prediction

    Department of Computer Science, University of California, Berkeley, Berkeley, California 94720, USA · Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, California 94720, USA · Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA +3

  2. HorizON: A Multi-Horizon Deep Operator Network for Property Prediction of Organic Battery Stacks

    Oct 27, 2026Subhash V. S. Ganti, Lukas Woelfel, Arla Diengdoh +4Materials Property PredictionDeep Operator Networks

    University of Bayreuth, Faculty of Engineering Science, Universitätsstraße 30, 95447 Bayreuth, Germany · University of Bayreuth, Bavarian Center for Battery Technology (BayBatt), Universitätsstraße 30, 95447 Bayreuth, Germany · Digital R&D, Covestro Deutschland AG, Kaiser-Wilhelm-Allee 60, 51373 Leverkusen, Germany

  3. Expert lithological labels do not improve learning when used to predict marine sediment properties

    Oct 26, 2026John M. Aiken, William Gilpin, Thorsten W. BeckerMaterials Property PredictionSelf-Supervised Learning

    University of Oslo, Oslo, Norway · Department of Physics, University of Texas at Austin, Austin, TX, USA · Institute for Geophysics, Jackson School of Geosciences, University of Texas at Austin, Austin, TX, USA +1

  4. Physical structure as an organizing principle for universal Hamiltonian learning

    Oct 26, 2026Mingkang Liu, Zhanghao Zhouyin, Guanghui Wang +3Computational Materials SciencePhysics-Informed ML

    Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215123, China · Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117575, Singapore · Department of Physics, McGill University, Montreal, Quebec H3A 2T8, Canada +4

  5. Deep Scientific Reasoning under Physical Constraints: Structure-Aware Spectrum Prediction

    Oct 25, 2026Yingheng Wang, Tao Yu, Shufeng Kong +3Materials Property PredictionPhysics-Informed ML

    Department of Computer Science, Cornell University · Griffith University · Institute of Condensed Matter and Nanosciences, Universit´e catholique de Louvain +3

  6. Probing the limits of forward screening and inverse design of crystal materials

    Oct 23, 2026Zhilong Song, Zijun Pang, Lixue ChengMaterials DiscoveryInverse Design

    Department of Chemistry, Hong Kong University of Science and Technology, Kowloon, Hong Kong 999077, China · IAS Center for AI for Scientific Discoveries, Hong Kong University of Science and Technology, Kowloon, Hong Kong 999077, China · Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong 999077, China

  7. AutoPhyX: Automatic Text-Condition Physics Property Generation

    Oct 21, 2026Bei Huang, Yixin Chen, Hongbin Zha +2Materials Property PredictionPhysics-Based Simulation

    State Key Laboratory of GAI, School of IST, Peking University, China · State Key Laboratory of General Artificial Intelligence, BIGAI, China · School of Artificial Intelligence and Computer Science, Anqing Normal University

  8. Learning Inter-Graph Interactions Between Heterogeneous Substructures of Chemical Systems

    Oct 20, 2026Gyoung S. NaGraph Representation LearningGraph Neural Networks

    Korea Research Institute of Chemical Technology

  9. Learning the Geometry of Materials Space with Trained Neural Tangent Kernels

    Oct 20, 2026Daniel Kaplan, Gabriel Kotliar, Anirvan M SenguptaNeural Representation GeometryComputational Materials Science

    Center for Materials Theory, Department of Physics and Astronomy, Rutgers University, Piscataway 08854 New Jersey USA · Department of Physics and Astronomy, Rutgers University, Piscataway 08854 New Jersey USA · Department of Physics, University of Central Florida, Orlando, Florida 32816, USA +3

  10. Explainable Machine Learning Analysis of Temperature Effects on Mechanical Strength of Fiber-Reinforced High-Performance Concrete

    Oct 16, 2026Jagaran Chakma, Zhiguang Zhou, Badhan Chakma +1Materials Property PredictionStructural Engineering

    State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China · School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China

  11. Scalable long-range machine learning interatomic potentials with electrical response for atomistic material modeling

    Oct 16, 2026Yicheng Chen, Xiaoyun Yu, Hyejeong Song +5Machine Learning Interatomic PotentialsComputational Materials Science

    Department of Materials Science and Engineering, National University of Singapore, Singapore · Department of Physics, National University of Singapore, Singapore

  12. Beyond DFT electronic band structures via dual-space orbital learning

    Oct 14, 2026Minhyuk Kang, Beom Seok Kang, Nikolai Argatoff +4Computational Materials ScienceMaterials Property Prediction

    Division of Engineering and Applied Science, California Institute of Technology · Department of Chemical Engineering, Imperial College London · Division of Chemistry and Chemical Engineering, California Institute of Technology +1

  13. Virtual-Alloy Physics-Aware Semantic System for extrapolative prediction of perovskite solar cells

    Oct 13, 2026Qiwei Han, Chi ZhouMaterials Property PredictionPhysics-Informed ML

    Department of Chemistry, Duke University, Durham, NC 27708, USA · Department of Computer Science, Georgia Institute of Technology, Atlanta, GA 30332, USA

  14. Physical-Field-Bridged Material Parameter Inference from Multi-View Deformation Videos

    Oct 13, 2026Linrui Shou, Jie Li, Yu Liu +1Materials Property PredictionInverse Problems

    Shanghai Jiao Tong University · University of Science and Technology Beijing · Peking University

  15. Comprehensive Workflow for Uncertainty-Aware Ionic Liquid Solvent Discovery

    Oct 12, 2026Alexander Lobo, Urvi Awasthi, Leonid ZhukovMaterials Property PredictionMaterials Science

    BCG X AI Science Institute

  16. Deep Learning-based Mechanical Characterization of Synthetically Generated Plant Fibers under Transverse Compression

    Oct 12, 2026Nikolaos Kegkeroglou, Bilal Sabillah, Myrto Inglezou +3Materials Property PredictionStructural Mechanics

    Tech Hive Labs, Iliados 7, Halandri, 152 32, Greece. · Université Marie et Louis Pasteur, SUPMICROTECH, CNRS, Institut FEMTO-ST, Besançon, F-25000, France. · AI Innovation Centre, University of Essex, Little Abington, Cambridge, CB21 6AL, UK.

  17. Cross-Material Magnetic Core Loss Prediction via Operating-Condition Matching and Partial Optimal Transport

    Oct 10, 2026Cong Yao, Yu Zheng, Zehao Hou +1Cross-Domain Transfer LearningMaterials Property Prediction

    Changsha University of Science and Technology, Changsha, China · School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, China · University of Arkansas, Fayetteville, AR 72701, USA

  18. PhysForge: Material-Controllable 3D Reconstruction with Separable Components

    Oct 9, 2026Yuchen Wang, Yanqi Bao, Zheng Gu +33D ReconstructionPhysics-Based Simulation

    School of Computer Science Nanjing University Jiangsu, 210023, China · Horizon Robotic Shanghai, China

  19. Provenance-grouped protocol for evaluating machine learning in perovskite photovoltaics

    Oct 8, 2026Qiwei Han, Chi ZhouBenchmark ValidityData Leakage

    Department of Chemistry, Duke University, Durham, NC 27708, USA · Department of Computer Science, Georgia Institute of Technology, Atlanta, GA 30332, USA

  20. Atom-JEPA: Joint-Embedding Predictive Architecture for 3D Atomistic Systems

    Oct 6, 2026Kasper Helverskov Petersen, Rasmus Hannibal Tirsgaard, François R J Cornet +2Joint-Embedding Predictive ArchitectureMaterials Property Prediction

    Department of Applied Mathematics and Computer Science Technical University of Denmark

  21. Mathematical Invariant-Enabled Topological Neural Networks for Molecular and Materials Property Prediction

    Oct 6, 2026Yiming Ren, Xiang Liu, Mustafa Hajij +2Materials Property PredictionMolecular Property Prediction

    Department of Mathematics, University of Georgia, Athens, GA 30602, USA. · Department of Data Science, University of San Francisco, CA 94117, USA. · Department of Computer Science and Technology, Cambridge University, Cambridge, United Kingdom. +2

  22. ImpactMat: Continuous Material Estimation for Inverse Impact Sound Rendering

    Oct 5, 2026Hyebin Cho, Bumsoo Kim, Joon Son ChungInverse RenderingMaterials Property Prediction

    Korea Advanced Institute of Science and Technology, South Korea · Chung-Ang University, South Korea

  23. CrystalJev: calibrated foundation-model probabilities decide what to compute next in materials discovery

    Oct 4, 2026Peng Kang, Yaxuan Sun, Zhen Li +3Materials DiscoveryMaterials Property Prediction

    School of Materials Science and Engineering, Beihang University, Beijing 100191, China · National Key Laboratory of Artificial Intelligence for Material Science, Beihang University, Beijing 100191, China

  24. ElectrolyteFM: Unifying Electrolyte Property Prediction through Cross-Property Knowledge Learning

    Sep 30, 2026Jiaxin Yu, Shuo Wang, Peng Wang +2Multi-Task LearningMaterials Property Prediction

    VoltaAI, Beijing, China · Renmin University of China, Beijing, China

  25. Measuring trainable degrees of freedom in materials graph neural networks: a random-subspace intrinsic dimension analysis

    Sep 28, 2026Shehroz Ahmad Shoaib, Kangming LiIntrinsic DimensionalityGraph Neural Networks

    Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. · Department of Electrical Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.

  26. Retrainable physics-integrated neural differentiable modeling of sintering across material systems

    Sep 25, 2026Zeping Chen, Ani Aprahamian, Khachatur V. Manukyan +1Materials Property PredictionComputational Materials Science

    Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, USA · Nuclear Science Laboratory, Department of Physics & Astronomy, University of Notre Dame, Notre Dame, IN, USA · Department of Chemistry & Biochemistry, University of Notre Dame, Notre Dame, IN, USA +1

  27. Growth-Inspired Graph Generation and Inverse Design of Mechanical Lattices via Dot Matrices Database Augmentation and GCNN

    Sep 24, 2026Weiyun Xu, Jiamu LiuGraph GenerationStructural Mechanics

    Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA · State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China

  28. An open benchmark for machine learning-based polymer property prediction

    Sep 22, 2026Robert W. Learsch, Nicholas Liesen, Daniel S. Levine +2Materials Property PredictionPolymer Property Prediction

    Lawrence Livermore National Laboratory · FAIR at Meta

  29. Seeing the Heat: Synthesizing High-Resolution Wood Thermal Responses from Optical Imagery

    Sep 22, 2026Jingren XieMaterials Property PredictionThermal Imaging

  30. Physics-residual machine learning predicts oxygen-evolution catalyst activity beyond the training range from sparse polarization measurements

    Sep 20, 2026Yong-Woon Kim, Jihyeok Lee, Sungtae Park +2OOD GeneralizationMaterials Property Prediction

    Department of Computer Engineering, Jeju National University, Jeju 63243, South Korea · Green Hydrogen Global Leading Research Center, Jeju National University, Jeju 63243, South Korea · Faculty of Applied Energy System, Jeju National University, Jeju 63243, South Korea +2