Computational Materials Science

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Overview

Computational materials science studies how composition, structure, and processing determine material properties, using quantum calculations, atomistic simulations, and continuum models. Common approaches include density functional theory (DFT), machine-learned interatomic potentials, equivariant graph neural networks, and surrogate or generative models for crystals, defects, alloys, and microstructures. These methods support property prediction, materials screening, and inverse design, with benchmarks such as Materials Project and OC20 testing bulk properties and surface energies and forces. A central challenge is balancing speed and scale with physical consistency, transfer to unfamiliar materials and conditions, and validation against experiments rather than computational reference data alone.

Latest papers

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145 papers16 papers in the last four weeks, down 20% on the four weeks before. 0.1% of all new papers.

  1. 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

  2. Accelerating SO(2)-Equivariant Materials Models through Low-Precision Emulation

    Oct 26, 2026Denghui Lu, Alexander Maeder, Alexandros Nikolaos Ziogas +2Equivariant Neural NetworksEquivariant GNNs

    D-ITET, ETH Zurich, Zurich, Switzerland

  3. GBClaw: An agentic AI framework for atomistic grain boundary research and mechanistic discovery

    Oct 20, 2026Yazhuo Liu, Kunqing Ding, Xiaoguo Gong +8Materials ScienceAI Agents for Scientific Discovery

    Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA · Department of Mechanical Science and Bioengineering, Graduate School of Engineering Science, The University of Osaka, Osaka, 560-8531, Japan · Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA, USA +2

  4. 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

  5. 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

  6. 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

  7. Risk-Controlled DFT Selection for Machine-Learned Interatomic Potentials

    Oct 14, 2026Kun Ryu, Yeo Jin Jung, Shyue Ping Ong +2Selective PredictionComputational Materials Science

    Pritzker School of Molecular Engineering, The University of Chicago · Department of Statistics, The University of Chicago · Department of Material Science and Engineering, National University of Singapore

  8. All-electron machine-learned orbital-free density functional for periodic CHNO systems

    Oct 13, 2026Marc K. Ickler, Andreas Hermann, Fred A. HamprechtDensity Functional TheoryComputational Quantum Chemistry

    Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, 69120, Germany. · Centre for Science at Extreme Conditions and SUPA, School of Physics and Astronomy, University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom.

  9. AtomWorld-Mirror: Macro-Step World Modeling of Critical Evolution Backbones for Materials Dynamics

    Oct 8, 2026Ziming Pan, Ruge Zhang, Haozhi Han +7Physics-Based SimulationComputational Materials Science

    Yonsei University, Seoul, Republic of Korea · Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China · Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China +4

  10. NEMORA: Neural Equivariant Multipole Operators for Long-Range Atomistic Learning

    Oct 7, 2026Jay L. Kaplan, Samuel Varner, Rebecca Willett +1Machine Learning Interatomic PotentialsEquivariant GNNs

    Courant Institute School of Mathematics, Computing, and Data Science, New York University, New York, NY 10012, USA · Department of Chemical and Biomolecular Engineering, Tandon School of Engineering, New York University, Brooklyn, NY 11201, USA · Department of Statistics, The University of Chicago, Chicago, IL 60637, USA +3

  11. Physics-Aligned Electronic Ground-State Learning Improves Generalization

    Oct 7, 2026Eike S. Eberhard, Xaver Kainz, Viktor Kotsev +2Physics-Informed MLOOD Generalization

    Technical University of Munich (TUM) · Munich Data Science Institute (MDSI) · Munich Center for Machine Learning (MCML) +1

  12. OxiGen: Oxidation-State-Aware Crystal Generation

    Oct 6, 2026Dylan John, Kim E. Jelfs, Alex M. Ganose +1Constrained Generative ModelingCrystal Structure Generation

    Department of Chemistry, Imperial College London · Department of Electrical and Electronic Engineering, Imperial College London

  13. Which alloy composition,what process parameters? Inferring the recipe from optimized metallic microstructure and texture

    Oct 6, 2026Mahish K. Guru, Jan Bohlen, Louam Lemjid +3Inverse DesignMaterials Science

    Institute of Material and Process Design, Helmholtz-Zentrum Hereon, Germany · Institute of Production Technology and Systems, Leuphana University Lüneburg, Germany · Institute of Material Systems Modeling, Helmholtz-Zentrum Hereon, Germany +2

  14. On architectural choices for interpretability and thermodynamic consistency in Physically Recurrent Neural Networks in the low-data regime

    Oct 2, 2026M. A. Maia, K. A. Meyer, A. M. C. M. van Gils +2Recurrent Neural NetworksNeural Network Interpretability

    Delft University of Technology, Department of Civil Engineering and Geosciences, P.O.Box 5048, 2600GA, Delft, The Netherlands · Chalmers University of Technology, Department of Mechanical Engineering, Gothenburg, Sweden

  15. BranchIP: Learning Adaptive Equivariant Computation for Interatomic Potentials

    Oct 1, 2026Laura Zichi, Gil Harari, Chuin Wei Tan +6Machine Learning Interatomic PotentialsEquivariant Neural Networks

    Harvard University · Robert Bosch LLC Research and Technology Center

  16. EP-Flow: Disordered Crystal Structure Prediction without Site-Level Annotations

    Oct 1, 2026Qiuliang Liu, Liming Wu, Qi Li +5Constrained Generative ModelingFlow Matching

    Institute of Physics, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · Gaoling School of Artificial Intelligence, Renmin University of China

  17. CompMat-Bench: Benchmarking AI Agents for Computational Materials Science

    Sep 30, 2026Chenmu Zhang, Levi Felix, Jun-Jie Zhang +6AI Agents for Scientific DiscoveryAI Agent Evaluation

    Department of Materials Science and NanoEngineering Rice University, Houston, TX 77005, USA

  18. A strategic roadmap for an atomistic machine-learning ecosystem

    Sep 30, 2026Jörg Behler, Michele Ceriotti, Cecilia Clementi +47Machine Learning Interatomic PotentialsScientific ML

  19. 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

  20. Scaling Density Functional Theory with Gaussian Splatting

    Sep 25, 2026Andrés Guzmán-Cordero, Cindy Zhang, Majdi Hassan +3Density Functional TheoryComputational Quantum Chemistry

    Mila - Quebec AI Institute · Université de Montréal · Princeton University +2

  21. AtomWorld-Mem: Memory-Restored World States for Long-Horizon Atomistic Evolution

    Sep 25, 2026Tian Luo, Ruge Zhang, Haozhi Han +5Hierarchical MemoryLatent World Models

    Sichuan University, Chengdu, China · Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China · Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China +2

  22. SoLiD26: A First Principles Solid-Liquid Interface Dataset for Machine-learned Interatomic Potentials

    Sep 23, 2026Jonas Busk, Emil J. P. Frost, Yogeshwaran Krishnan +7Machine Learning Interatomic PotentialsMaterials Science

    Department of Energy Conversion and Storage, Technical University of Denmark, Kongens Lyngby, Denmark. · Pioneer Center for Accelerating P2X Materials Discovery (CAPeX), Technical University of Denmark, Kongens Lyngby, Denmark.

  23. Divergent strategies and convergent outcomes in autonomous materials discovery

    Sep 21, 2026Jihan KimAutonomous Scientific DiscoveryAutonomous Research Agents

    Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.

  24. Complete Neural Electronic Initialization Accelerates Materials DFT

    Sep 18, 2026Felix Ærtebjerg, Jonas Elsborg, Arghya BhowmikQuantum ChemistryComputational Materials Science

    Department of Energy Conversion and Storage, Technical University of Denmark

  25. A Hybrid Quantum Neural Network to Analyse Big Experimental Powder X-ray Diffraction Data

    Sep 17, 2026H. Dong, S. D. M. Jacques, M. Q. Hlatshwayo +4Quantum Neural NetworksHybrid Quantum-Classical ML

    Finden ltd, Building R70, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, OX11 0QX, UK · National Quantum Computing Centre, Harwell Science and Innovation Campus, OX11 0QX, UK · Department of Chemistry, University College London, 20 Gordon Street, WC1H 0AJ, UK +2

  26. CrystalMO-TuRBO: Multi-Objective Trust-Region Bayesian Optimization for High-precision Joint Crystal Structure Refinement

    Sep 17, 2026Joseph Agada, Yishu Wang, Arpan BiswasBayesian OptimizationTrust-Region Optimization

    University of Tennessee - Oak Ridge Innovation Institute University of Tennessee Knoxville, TN 37996 · Department of Materials Science and Engineering University of Tennessee Knoxville, TN 37996

  27. Truncated automatic sparse differentiation for machine learning interatomic potentials

    Sep 17, 2026Marcel F. Langer, Adrian Hill, Michele CeriottiAutomatic DifferentiationMachine Learning Interatomic Potentials

    Laboratory of Computational Science and Modeling (COSMO), EPFL, Lausanne, Switzerland · Machine Learning Group, Technical University of Berlin, Berlin, Germany · BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany

  28. A literature-guided descriptor-based framework for filtering composition search spaces

    Sep 14, 2026Lei Zhang, Markus StrickerMaterials DiscoveryComputational Materials Science

    Interdisciplinary Centre for Advanced Materials Simulation, Ruhr University Bochum, Universitätsstraße 150, 44780 Bochum, North Rhine-Westphalia, Germany

  29. Neural-Network Solutions to Real-Space Charge Density and Generalization

    Sep 14, 2026Yuxuan Zeng, Tong Xie, Taoyuze Lv +1Neural Surrogate ModelingEquivariant Neural Networks

    School of Artificial Intelligence & Data Science, University of Science and Technology of China · Suzhou Institute for Advanced Research, University of Science and Technology of China

  30. Prescreening Point Defects in Semiconductors With Machine Learning

    Sep 13, 2026Paul Karlsson, Joel Davidsson, Rickard ArmientoMaterials Property PredictionPhysics-Informed ML

    Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden