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. A self-evolving agent for explainable diagnosis of DFT-experiment band-gap mismatch

    Apr 29, 2026Yue Li, Bijun TangAI Agents for Scientific DiscoveryMaterials Science

    School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.

  2. Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations

    Apr 28, 2026Gabriel de Miranda Nascimento, Marc L. Descoteaux, Laura Zichi +9Machine Learning Interatomic PotentialsMultifidelity Modeling

    Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA · John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA · Robert Bosch LLC Research and Technology Center, Watertown, MA, USA

  3. Kohn-Sham Hamiltonian from Effective Field Theory: Quasiparticle Band Narrowing from Frozen Core Dynamics

    Apr 28, 2026Xiansheng Cai, Han Wang, Kun ChenDensity Functional TheoryQuantum Many-Body Physics

    Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China

  4. Transferable SCF-Acceleration through Solver-Aligned Initialization Learning

    Apr 23, 2026Eike S. Eberhard, Viktor Kotsev, Timm Güthle +1Quantum ChemistryDensity Functional Theory

    1Technical University of Munich (TUM) · 2Munich Data Science Institute (MDSI) · 3Munich Center for Machine Learning (MCML)

  5. Generative Discovery of Magnetic Insulators under Competing Physical Constraints

    Apr 22, 2026Qiulin Zeng, Tahiya Chowdhury, Md Shafayat HossainMaterials ScienceCrystal Structure Generation

    Department of Math, University of Southern California, Los Angeles, CA, 90007 · Department of Computer Science, Colby College, Waterville, ME, 04901 · Department of Materials Science and Engineering, University of California, Los Angeles, Los Angeles, CA 90095, United States +1

  6. Expanding the extreme-k dielectric materials space through physics-validated generative reasoning

    Apr 22, 2026Hossain Hridoy, Tahiya Chowdhury, Md Shafayat HossainMaterials ScienceLarge Language Model-Guided Scientific Discovery

    Department of Chemical Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. · Department of Computer Science, Colby College, Waterville, ME, USA. · Department of Materials Science and Engineering, University of California, Los Angeles, CA, USA. +2

  7. Surrogate Functionals for Machine-Learned Orbital-Free Density Functional Theory

    Apr 22, 2026Roman Remme, Fred A. HamprechtSurrogate ModelingQuantum Chemistry

    Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University

  8. LLM-guided phase diagram construction through high-throughput experimentation

    Apr 22, 2026Ryo Tamura, Haruhiko Morito, Yuna Oikawa +7Materials ScienceLarge Language Model-Guided Scientific Discovery

    Center for Basic Research on Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan · Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8561, Japan · RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan +8

  9. ChemGraph-XANES: An Agentic Framework for XANES Simulation and Curation

    Apr 17, 2026Vitor F. Grizzi, Thang Duc Pham, Luke N. Pretzie +3Scientific Workflow AutomationComputational Materials Science

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

  10. Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials

    Apr 17, 2026Yuanchang Zhou, Hongyu Wang, Yiming Du +12Machine Learning Interatomic PotentialsCommunication-Efficient Distributed Training

    State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences · Independent Researcher · Sun Yat-Sen University

  11. Finetuning-Free Diffusion Model with Adaptive Constraint Guidance for Inorganic Crystal Structure Generation

    Apr 14, 2026Auguste de Lambilly, Vladimir Baturin, David Portehault +4Diffusion Model GuidanceCrystal Structure Generation

    CNRS-Saint-Gobain-NIMS, IRL 3629, Laboratory for Innovative Key Materials and Structures (LINK), 1-1 Namiki, 305-0044 Tsukuba, Japan · LTCI, Télécom Paris, Institut Polytechnique de Paris, France · Laboratoire de Chimie de la Matière Condensée de Paris (LCMCP), Sorbonne Université, CNRS, 4 place Jussieu, F-75005 Paris, France +2

  12. 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

  13. Inverse Design of Inorganic Compounds with Generative AI

    Apr 11, 2026Hannes Kneiding, Lucía Morán-González, Nishamol Kuriakose +2Inverse DesignMolecular Generation

    Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, 0315 Oslo, Norway

  14. 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

  15. EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers

    Apr 10, 2026Yi-Lun Liao, Alexander J. Hoffman, Sabrina C. Shen +3Equivariant GNNsGraph Transformer

    Massachusetts Institute of Technology (MIT) · Mirror Physics · Entalpic

  16. Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks

    Apr 9, 2026Eunjeong Park, Amrita BasakComputational Materials SciencePhysics-Informed GNNs

    Department of Mechanical Engineering, Pennsylvania State University, University Park, PA, 16802, USA

  17. 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

  18. 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

  19. Ontology-based knowledge graph infrastructure for interoperable atomistic simulation data

    Mar 31, 2026Abril Azocar Guzman, Sarath Menon, Tilmann Hickel +1KG ConstructionKnowledge Representation

    Institute for Advanced Simulations – Materials Data Science and Informatics (IAS-9), Forschungszentrum Jülich GmbH, Jülich, Germany · Interdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr University Bochum, Bochum, Germany · Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany

  20. Perspective: Towards sustainable exploration of chemical spaces with machine learning

    Mar 31, 2026Leonardo Medrano Sandonas, David Balcells, Anton Bochkarev +20AI-Assisted Scientific ResearchEnergy-Efficient ML

    Institute for Materials Science and Max Bergmann Center of Biomaterials, TUD Dresden University of Technology, 01062, Dresden, Germany · Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, 0315 Oslo, Norway · Interdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr-University Bochum, 44780 Bochum, Germany +23

  21. SimMOF: AI agent for Automated MOF Simulations

    Mar 31, 2026Jaewoong Lee, Taeun Bae, Jihan KimScientific Workflow AutomationMaterials Science

    Department of chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea

  22. AI-ready design of realistic 2D materials and interfaces with Mat3ra-2D

    Mar 29, 2026Vsevolod Biryukov, Kamal Choudhary, Timur BazhirovMaterials ScienceComputational Materials Science

    Exabyte Inc. (Mat3ra.com), Walnut Creek, CA 94596, USA · 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

  23. Hessian-informed machine learning interatomic potential towards bridging theory and experiments

    Mar 26, 2026Bangchen Yin, Jian Ouyang, Zhen Fan +7Machine Learning Interatomic PotentialsComputational Materials Science

    ByteDance Seed · Tsinghua University · Peking University

  24. ChargeFlow: Flow-Matching Refinement of Charge-Conditioned Electron Densities

    Mar 25, 2026Tri Minh Nguyen, Sherif Abdulkader Tawfik, Truyen Tran +1Computational Materials ScienceQuantum Chemistry

    Deakin University, Geelong, VIC 3216, Australia

  25. AutoMOOSE: An Agentic AI for Autonomous Phase-Field Simulation

    Mar 22, 2026Sukriti Manna, Henry Chan, Subramanian K. R. S. SankaranarayananComputational Materials ScienceAI Agents for Scientific Discovery

    Department of Mechanical and Industrial Engineering, University of Illinois Chicago, Chicago, IL 60607, USA · Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL 60439, USA

  26. Physics-Informed Long-Range Coulomb Correction for Machine-learning Hamiltonians

    Mar 20, 2026Yang Zhong, Xiwen Li, Xingao Gong +1Computational Materials SciencePhysics-Informed GNNs

    Key Laboratory of Computational Physical Sciences (Ministry of Education), Institute of Computational Physical Sciences, State Key Laboratory of Surface Physics, and Department of Physics, Fudan University, Shanghai, 200433, China.

  27. An SO(3)-equivariant reciprocal-space neural potential for long-range interactions

    Mar 19, 2026Lingfeng Zhang, Taoyong Cui, Dongzhan Zhou +6Machine Learning Interatomic PotentialsComputational Materials Science

    The Chinese University of Hong Kong, Hong Kong, 999077, China. · Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, 999077, China. · Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China. +1

  28. A Kolmogorov-Arnold Surrogate Model for Chemical Equilibria: Application to Solid Solutions

    Mar 16, 2026Leonardo Boledi, Dirk Bosbach, Jenna PoonoosamyNeural Surrogate ModelingKolmogorov-Arnold Networks

    Institute of Fusion Energy and Nuclear Waste Management (IFN-2), Forschungszentrum Jülich GmbH

  29. Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials

    Mar 16, 2026William J. Baldwin, Ilyes Batatia, Martin Vondrák +2Density Functional TheoryMachine Learning Interatomic Potentials

    Engineering Laboratory, University of Cambridge, Trumpington St, Cambridge, UK · University of Bayreuth, Bavarian Center for Battery Technology (BayBatt), Bayreuth, Germany · Fritz-Haber-Institute of the Max-Planck-Society, Berlin, Germany

  30. Scaling Autoregressive Models for Lattice Thermodynamics

    Mar 16, 2026Xiaochen Du, Juno Nam, Sulin Liu +1Autoregressive ModelingComputational Materials Science

    Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA