Computational Materials Science

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15 papers in the last four weeks, up 400% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 86

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MALOQ: Massively Accelerated Learning of Operators for Quantum Transport
Jun 27, 2026Manasa Kaniselvan, Alexander Maeder, Denghui Lu +2Quantum Chemistry High-Performance Computing
D-ITET, ETH Zurich, Zurich, Switzerland
A Reduced Order Model for Emergent Mechanics in Woven Systems
Jun 22, 2026Anvay A. Pradhan, Evgueni T. Filipov, Talia Y. MooreStructural Mechanics Materials Science
Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, 48109, USA · Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, 48109, USA · Department of Robotics, University of Michigan, Ann Arbor, Michigan, 48109, USA +1
Deep material network for homogenization of piezoelectric composites
Jun 21, 2026Ting-Ju Wei, Yen-Ming Lu, Chuin-Shan ChenNeural Surrogate Modeling Materials Science
Department of Civil Engineering National Taiwan University Taipei, Taiwan · Department of Civil Engineering Department of Materials Science and Engineering National Taiwan University Taipei, Taiwan
Mat-Pref: Verifiable-Reward Training Improves Compositional Reasoning in Inorganic Materials
Jun 20, 2026Sarrah R. Mikhail Leung, Taehan Kim, Jeongbin ParkCompositional Reasoning RL for Language Model Reasoning
University of California, Berkeley · 2Machine Learning at Berkeley · University of Michigan
Computational references are not experiments: pre-registered validation of machine-learned sodium-cathode voltages
Jun 19, 2026Krishna Teja VepaMaterials Property Prediction Virtual Screening
Independent Researcher†
Equivariant Graph Neural Networks Improve Optical Spectra Prediction for Materials Screening
Jun 17, 2026Kasper Helverskov Petersen, François R J Cornet, Martin Ovesen +3Materials Property Prediction Equivariant GNNs
Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark · Department of Physics, Technical University of Denmark, Kongens Lyngby, Denmark
InvDesMobility: a reliability-gated first-principles feedback framework for closed-loop materials discovery
Jun 15, 2026Wen-Kao Li, Ze-Feng Gao, Peng-Jie Guo +2Materials Property Prediction Autonomous Scientific Discovery
School of Physics and Key Laboratory of Quantum State Construction (Ministry of Education), Renmin University of China, Beijing 100872, China
Scalar-pathway fidelity improves physical accuracy in short-range equivariant interatomic potentials
Jun 14, 2026Jia Bi, Alin Marin Elena, Samuel PinillaMachine Learning Interatomic Potentials Equivariant GNNs
Science and Technology Facilities Council, Harwell Campus, Didcot, OX11 0QX, Oxfordshire, United Kingdom. · Science and Technology Facilities Council, Keckwick Lane, Daresbury, WA4 4AD, Cheshire, United Kingdom. · Diamond Light Source, Harwell Science and Innovation Campus,2026 Didcot, OX11 0QX, Oxfordshire, United Kingdom.
A Hybrid GNN-FEM Framework for Phase-Field Fracture Simulation. Physics-Preserving Hybridization for Generalizable Surrogate Modeling
Jun 12, 2026Hyeonbin Moon, Yongjin Choi, Seunghwa RyuStructural Mechanics PDE Surrogate Modeling
Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea · KAIST InnoCORE PRISM-AI Center, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea · Department of AX, College of AI, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
XRDiff: Crystal Structure Prediction from Powder X-Ray Diffraction Data Using Diffusion Models
Jun 12, 2026Nofit Segal, Mingda Li, Benjamin Kurt Miller +1Crystal Structure Prediction Diffusion Models
Department of Materials Science and Engineering, MIT, Cambridge, MA, USA. · Department of Nuclear Science and Engineering, MIT, Cambridge, MA, USA. · FAIR, Meta, San Francisco, CA, USA.
Magnetic HIP-NN for spin dynamics in disordered itinerant magnets
Jun 9, 2026Supriyo Ghosh, Yunhao Fan, Sheng Zhang +2Equivariant Neural Networks Message Passing Neural Networks
Department of Physics, University of Virginia, Charlottesville, Virginia 22904, USA · Department of Chemistry, University of Chicago, Chicago, IL 60637, USA · Theoretical Division and CNLS, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
Agentic multi-fidelity learning of quasiparticle and excitonic properties
Jun 5, 2026Arnab Neogi, Aaron Forde, Christopher A. Lane +2AI Agents for Scientific Discovery Materials Property Prediction
Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA · Center for Integrated Nanotechnologies, Materials Physics and Applications Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
Decision-Aware Evaluation of Physics-Informed Surrogates
Jun 5, 2026Daniel Cieślak, Andrzej CzyżewskiSurrogate Modeling Benchmark Design
Gdańsk University of Technology, Gdańsk, Poland
Derivative Informed Learning of Exchange-Correlation Functionals
Jun 2, 2026Eike S. Eberhard, Luca A. Thiede, Abdul Aldossary +5Quantum Chemistry Knowledge Distillation
Technical University of Munich, Munich, Germany · Munich Data Science Institute, Munich, Germany · Munich Center for Machine Learning, Munich, Germany +4
Machine Learning Surrogate Modeling for Homogenization of Hyperelastic Materials with Boolean Microstructures
May 31, 2026Matthias Brändel, Oliver RheinbachNeural Surrogate Modeling Hyperelasticity
Technische Universität Bergakademie Freiberg, Institut für Numerische Mathematik und Optimierung, Fakultät für Mathematik und Informatik, 09596 Freiberg, Germany.
Benchmark Dataset for Catalysis on 2D MXenes
May 30, 2026Pavlo Melnyk, Anmar Karmush, Mårten Wadenbäck +4Machine Learning Interatomic Potentials Benchmark Design
Computer Vision and Learning Systems, Department of Electrical Engineering (ISY) & AI4X, Linköping University (LiU), 58183 Linköping, Sweden. · Materials Design Division, Department of Physics, Chemistry and Biology (IFM), and Wallenberg Initiative Materials Science for Sustainability (WISE); Linköping University (LiU), 58183 Linköping, Sweden. · Wallenberg Initiative Materials Science for Sustainability (WISE); Linköping University (LiU), 58183 Linköping, Sweden.
Discovering Thermodynamically Admissible Dissipation Potentials via Grammar-Based Symbolic Regression
May 29, 2026Federico Califano, Jacopo CiambellaMaterials Science Computational Materials Science
LEIA: Learned Environment for Interactive Architected Materials
May 27, 2026Haiqian Yang, Yuan Cao, Markus J. BuehlerNeural Surrogate Modeling World Model Learning
Unreasonable Labs · Mountain View, CA 94043, USA
AutoDFT: A Closed-Loop Multi-Agent Framework for Autonomous DFT Calculations
May 25, 2026Penghui Yang, Zhonghan Zhang, Yue Li +6Multi-Agent LLM Systems Scientific Workflow Automation
Nanyang Technological University, Singapore · Singapore Management University
SPLIT-PINN: Separable Probability Learning Technique via Physics-Informed Neural Networks for High-Dimensional Probabilistic Modeling
May 23, 2026Pouria Behnoudfar, Deekshith Naidu Ponnana, Noah J. Schmelzer +6Physics-Informed ML Computational Materials Science
Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI, USA · Department of Civil Engineering, Johns Hopkins University, Baltimore, MD, USA · Materials Physics and Applications Division, Los Alamos National Laboratory, Los Alamos, NM, USA +2
Representability-Aware Neural Networks for Reduced Density Matrices: Application to Fractional Chern Insulators
May 19, 2026Justin B. Hart, Awwab A. Azam, Thomas Li +4Quantum Machine Learning Quantum Many-Body Physics
Department of Physics, University of Florida, Gainesville, FL, USA · Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA · Palo Alto High School, Palo Alto, CA, USA +4
Harnessing AtomisticSkills for Agentic Atomistic Research
May 18, 2026Bowen Deng, Bohan Li, Matthew Cox +20AI Agents for Scientific Discovery AI Coding Agents
Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Department of Chemistry, Kookmin University, Seoul 02707, Republic of Korea +5
Atomistic Modeling of Chemical Disorder in Materials: Bridging Classical Methods and AI-Assisted Approaches
May 18, 2026Jiayu Peng, Peichen ZhongCrystal Structure Generation Computational Materials Science
Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY 14260, USA · Department of Materials Science and Engineering, National University of Singapore, Singapore 117575, Singapore
Reweighting free energy profiles between universal machine learning interatomic potentials for fast consensus building
May 15, 2026Sauradeep Majumdar, Miguel Steiner, Johannes C. B. Dietschreit +4Free Energy Calculation Machine Learning Interatomic Potentials
Department of Material Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, United States. · Institute of Theoretical Chemistry, University of Vienna, Währinger Str. 17, 1090, Vienna, Austria. · Department of Physical and Macromolecular Chemistry, Charles University, Hlavova 8, Praha 2, 12800, Czech Republic.
Crys-JEPA: Accelerating Crystal Discovery via Embedding Screening and Generative Refinement
May 14, 2026Nian Liu, Nikita Kazeev, Stephen Gregory Dale +8Joint-Embedding Predictive Architecture Generative Modeling
1National University of Singapore · 2Loyola Marymount University · 3New York University +1
Agentic Design of Compositional Descriptors via Autoresearch for Materials Science Applications
May 14, 2026Matteo Cobelli, Stefano SanvitoMaterials Property Prediction Automated Feature Engineering
School of Physics and CRANN Institute, Trinity College, Dublin 2, Dublin, Ireland.
Generating Symmetric Materials using Latent Flow Matching
May 11, 2026Anmar Karmush, Cedric Mathieu Brandenburg, Soheil Ershadrad +3Crystal Structure Generation Computational Materials Science
Department of Electrical Engineering (ISY) & AI4x, Linköping University · Department of Physics, Chemistry and Biology (IFM), Linköping University · Wallenberg Initiative Materials Science for Sustainability (WISE), Linköping University +1
Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators
May 10, 2026S. A. Shteingolts, Salman N. Salman, Dan MendelsGraph Neural Networks OOD Generalization
The Wolfson Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa, 32000, Israel
Physics-Informed Reduced-Order Operator Learning for Hyperelasticity in Continuum Micromechanics
May 8, 2026Hamidreza Eivazi, Henning WesselsStructural Mechanics Hyperelasticity
Division Data-Driven Modeling of Mechanical Systems, Institute of Applied Mechanics, Technische Universität Braunschweig, Pockelsstr. 3, 38106 Braunschweig, Germany
SLayerGen: a Crystal Generative Model for all Space and Layer Groups
May 7, 2026Rees Chang, Andrew Novick, Ryan P Adams +1Crystal Structure Generation Diffusion Models
Department of Materials Science & Engineering, University of Illinois at Urbana-Champaign · Department of Computer Science, Princeton University · Materials Research Laboratory, University of Illinois at Urbana-Champaign

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MALOQ: Massively Accelerated Learning of Operators for Quantum Transport

Manasa Kaniselvan, Alexander Maeder, Denghui Lu, Alexandros Nikolaos Ziogas+1

D-ITET, ETH Zurich, Zurich, Switzerland

Quantum Chemistry High-Performance Computing Distributed Training Computational Materials Science

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Machine-learned (ML) operator models can be trained to predict density functional theory (DFT) Hamiltonian/density matrices at significantly reduced computational cost, thus extending electronic-structure calculations to previously unfeasible scales. Here, we introduce MALOQ (Massively Accelerated Learning of Operators for Quantum Transport), an application built to train on and predict electronic-structure matrices for systems made of few to 100k atoms, described by large basis sets, and covering a wide range of atomic elements. Based on a state-of-the-art, SO(2)-equivariant backbone architecture, MALOQ provides (i) custom data-processing kernels to handle high-rank Hamiltonian matrix data and (ii) a scalable edge-wise distribution of atomic graph(s). Trained on the largest molecular Hamiltonian datasets available today, it reduces time-per-epoch by over 30% compared to a molecule-wise-distributed framework, and enables inference on material graphs of arbitrary size. We demonstrate scalable training and inference for 3,000-12,000 atoms on the Alps supercomputer, up to 192 GPUs and 256 GPUs, respectively.

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Latest papers 86