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. From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Physics

    Mar 13, 2026Haonan HuangAI Agents for Scientific DiscoveryComputational Materials Science

    Department of Physics, Princeton University, Princeton, NJ 08540, USA.

  2. Diffusion-based Generative Machine Learning Model for Predicting Crack Propagation in Aluminum Nitride at the Atomic Scale

    Mar 13, 2026Jiali Lu, Shengfeng YangMaterials ScienceComputational Materials Science

    Purdue University

  3. PolyCrysDiff: Controllable Generation of Three-Dimensional Computable Polycrystalline Material Structures

    Mar 12, 2026Chi Chen, Tianle Jiang, Xiaodong Wei +1Materials ScienceComputational Materials Science

    Global College, Shanghai Jiao Tong University, No.800 Dong Chuan Road, Shanghai, 200240, Shanghai, China. · Global Institute of Future Technology, Shanghai Jiao Tong University, No.800 Dong Chuan Road, Shanghai, 200240, Shanghai, China.

  4. Offline Materials Optimization with CliqueFlowmer

    Mar 6, 2026Jakub Grudzien Kuba, Benjamin Kurt Miller, Sergey Levine +1Materials DiscoveryComputational Materials Science

    BAIR, UC Berkeley, Berkeley, California, USA · FAIR, Meta, San Francisco, California, USA

  5. Predicting Atomistic Transitions with Transformers

    Mar 5, 2026Henry Tischler, Wenting Li, Qi Tang +2Computational Materials ScienceNeural Surrogate Modeling

    Computing and Artificial Intelligence Division, Los Alamos National Laboratory, Los Alamos, NM 87545 · School of Engineering and Computer Science, University of Denver, Denver, CO 80210 · Department of Physics and Astronomy, University of Denver, Denver, CO 80210 +4

  6. Lang2Str: Two-Stage Crystal Structure Generation with LLMs and Continuous Flow Models

    Mar 4, 2026Cong Liu, Chengyue Gong, Zhenyu Liu +2Crystal Structure GenerationComputational Materials Science

    AMLab, AI4Science Lab University of Amsterdam

  7. TritonDFT: Automating DFT with a Multi-Agent Framework

    Mar 2, 2026Zhengding Hu, Kuntal Talit, Zhen Wang +8Scientific Workflow AutomationDensity Functional Theory

    University of California, San Diego · University of California, Merced · Los Alamos National Laboratory

  8. MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interatomic Potentials

    Mar 2, 2026Yuanchang Zhou, Siyu Hu, Xiangyu Zhang +3Machine Learning Interatomic PotentialsComputational Materials Science

    State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences · School of Advanced Interdisciplinary Sciences, UCAS

  9. Discovery of Interpretable Physical Laws in Materials via Language-Model-Guided Symbolic Regression

    Feb 26, 2026Yifeng Guan, Chuyi Liu, Dongzhan Zhou +4Scientific DiscoverySymbolic Regression

    Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China. · School of Physics, Zhejiang University, Hangzhou, 310058, China. · Soochow Institute for Energy and Materials Innovations (SIEMIS), Soochow University, Suzhou, 215006, China. +3

  10. Constrained Diffusion for Accelerated Structure Relaxation of Inorganic Solids with Point Defects

    Feb 22, 2026Jingyi Cui, Jacob K. Christopher, Ankita Biswas +2Computational Materials ScienceMaterials Science

    University of Virginia

  11. OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs

    Feb 22, 2026Mohammadmahdi Vahediahmar, Matthew A. McDonald, Feng LiuCrystal Structure PredictionCrystal Structure Generation

    Department of Computer Science, Drexel University · Department of Chemical and Biological Engineering, Drexel University

  12. Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models

    Feb 19, 2026Chenyi Ji, Kian P. Abdolazizi, Hagen Holthusen +2Materials ScienceNeural Network Interpretability

    Computational Mechanics in Medicine, Applied Medical Engineering, RWTH Aachen University Pauwelsstraße 20, 52074 Aachen, Germany · Institute for Continuum and Material Mechanics, Hamburg University of Technology Eißendorfer Straße 42, 21073 Hamburg, Germany · Institute of Applied Mechanics, University of Erlangen-Nuremberg Egerlandstrasse 5, 91058 Erlangen, Germany +1

  13. A fully differentiable framework for training proxy Exchange Correlation Functionals for periodic systems

    Feb 17, 2026Rakshit Kumar Singh, Aryan Amit Barsainyan, Bharath RamsundarComputational Quantum ChemistryDensity Functional Theory

    Deep Forest Sciences

  14. Adaptive Uncertainty-Guided Surrogates for Efficient phase field Modeling of Dendritic Solidification

    Feb 17, 2026Eider Garate-Perez, Kerman López de Calle-Etxabe, Oihana Garcia +3Surrogate ModelingComputational Materials Science

    Intelligent Information Systems, Tekniker, Iñaki Goenaga 5, Eibar (20600) Spain · Faculty of Informatics UPV-EHU, Paseo Manuel de Lardizabal 1, Donostia (20700) Spain · Department of Computer Sciences and Artificial Intelligence, UPV-EHU, Donostia (20700) Spain +1

  15. Fast and accurate quasi-atom method for simultaneous atomistic and continuum simulation of solids

    Feb 16, 2026Artem Chuprov, Egor E. Nuzhin, Alexey A. Tsukanov +1Computational Materials ScienceMolecular Dynamics Simulation

    Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1, Moscow, 121205, Russia · Schmidt Institute of Physics of the Earth of the Russian Academy of Sciences B. Gruzinskaya str., 10, bld. 1, Moscow, 123242, Russia · School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK

  16. Predicting magnetism with first-principles AI

    Feb 9, 2026Max Geier, Liang FuMaterials Property PredictionMaterials Science

    Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

  17. VASP Agent: An Agentic Framework for Autonomous First-principles Calculations

    Dec 22, 2025Zeyu Xia, Jinzhe Ma, Congjie Zheng +11AI Coding AgentsComputational Materials Science

    Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China. · Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China. · School of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, China. +6

  18. Revisiting the Broken Symmetry Phase of Solid Hydrogen: A Neural Network Variational Monte Carlo Study

    Dec 19, 2025Shengdu Chai, Chen Lin, Xinyang Dong +4Variational Monte CarloCrystal Structure Prediction

    Interdisciplinary Center for Theoretical Physics and Information Sciences (ICTPIS), Fudan University, Shanghai 200433, China · Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China · Department of Engineering, University of Oxford, Oxford OX1 4BH, UK +4

  19. Predictive Inorganic Synthesis based on Machine Learning using Small Data sets: a case study of Hydrodynamic Diameter-controlled Cu Nanoparticles

    Dec 18, 2025Brent Motmans, Digvijay Ghogare, Thijs G. I. van Wijk +6Small-Data LearningMaterials Property Prediction

    Hasselt University, Institute for Materials Research (IUMAT), Quantum & Artificial inTelligence design Of Materials (QuATOMs), Martelarenlaan 42, B-3500 Hasselt, Belgium · Hasselt University, Institute for Materials Research (IUMAT), Hybrid Materials Design (HyMaD), Martelarenlaan 42, B-3500 Hasselt, Belgium · imec, IUMAT, Wetenschapspark 1, B-3590 Diepenbeek, Belgium +4

  20. AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org

    Dec 12, 2025Jaehyung Lee, Justin Ely, Kent Zhang +3AI Agents for Scientific DiscoveryLLM Agent Evaluation

    Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD 21218, USA · Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA · Department of Physics and Astronomy, West Virginia University, Morgantown, WV 26506, USA

  21. ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs

    Sep 28, 2025Evan Dramko, Yihuang Xiong, Yizhi Zhu +4Machine Learning Interatomic PotentialsMaterials Property Prediction

    Department of Computer Science, Rice University, Houston, TX, USA · Thayer School of Engineering, Dartmouth College, Hanover, NH, USA · Department of Materials Science and Nanoengineering, Rice University, Houston, TX, USA +2

  22. Shoot from the HIP: Hessian Interatomic Potentials without derivatives

    Sep 25, 2025Andreas Burger, Luca Thiede, Nikolaj Rønne +5Machine Learning Interatomic PotentialsComputational Materials Science

    University of Toronto · Vector Institute for Artificial Intelligence · Technical University of Denmark +4

  23. Learning Magnetic Order Classification from Large-Scale Materials Databases

    Sep 7, 2025Ahmed E. FahmyMaterials Property PredictionClassification

    Department of Physics, The Ohio State University, Columbus, OH 43210, USA

  24. Surprisingly High Redundancy in Electronic Structure Data Across Materials Explained by Low Intrinsic Dimensionality

    Jul 11, 2025Sazzad Hossain, Ponkrshnan Thiagarajan, Shashank Pathrudkar +4Intrinsic DimensionalityDataset Pruning

    Department of Mechanical and Aerospace Engineering, Michigan Technological University, Houghton, MI, USA. · Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, USA. · Department of Materials Science and Engineering, University of California, Los Angeles, CA, USA. +1

  25. Tensor-reduced atomic density representations

    Oct 2, 2022James P. Darby, Dávid P. Kovács, Ilyes Batatia +4Molecular Representation LearningInvariant Representation Learning

    Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry, CV4 7AL, UK · Engineering Laboratory, University of Cambridge, Cambridge, CB2 1PZ UK · ENS Paris-Saclay, Université Paris-Saclay, 91190 Gif-sur-Yvette, France +3