Materials Property Prediction

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9 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.

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Latest papers 103

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UniPixie: Unified and Probabilistic 3D Physics Learning via Flow Matching
Jun 3, 2026Qilin Huang, Quynh Anh Huynh, Long Le +5Materials Property Prediction Conditional Generative Modeling
University of Pennsylvania · Southern University of Science and Technology · Work done during an internship at UPenn +1
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach
May 31, 2026An Vuong, Minh-Hao Van, Chen Zhao +1Materials Property Prediction Materials Science
University of Arkansas · Baylor University
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.
Latent Diffusion Pretraining for Crystal Property Prediction
May 30, 2026Shrimon Mukherjee, Kishalay Das, Partha Basuchowdhuri +2Latent Diffusion Models Self-Supervised Pre-Training
School of Mathematical & Computational Sciences, Indian Association for the Cultivation of Science, Kolkata, India · Computer Science and Engineering, Indian Institute of Technology Kharagpur, India.
Mixing Vector Model for Copolymer Inference via Mixed Integer Linear Programming
May 28, 2026Jianshen Zhu, Raveena Rai, Taiyo Sohkawa +4Molecular Optimization Materials Property Prediction
Department of Information Science and Technology, Tokyo University of Science, Noda 278-8510, Japan · Discrete Mathematics and Computational Intelligence Laboratory, Department of Mathematics, Quaid-i-Azam University, Islamabad 45320, Pakistan · Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan +3
Thermodynamic properties of chemically disordered compounds via AI-driven estimation of partition function with the PULSE method
May 27, 2026Baptiste Bernard, Luca Messina, Eiji Kawasaki +1Materials Property Prediction Materials Science
aCEA DES IRESNE DEC Cadarache F-13108 Saint-Paul-Lez-Durance France · bUniversite Paris-Saclay CEA LIST F-91120 Palaiseau France
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
Periodic Topological Deep Learning for Polymer Design and Discovery
May 26, 2026Yasharth Yadav, Tze Kwang Gerald Er, Atsushi Goto +1Materials Property Prediction Molecular Representation Learning
School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371 · School of Chemistry, Chemical Engineering and Biotechnology (CCEB), Nanyang Technological University, Singapore 637371
PolyFusionAgent: A Multimodal Foundation Model and Autonomous AI Assistant for Polymer Property Prediction and Inverse Design
May 26, 2026Manpreet Kaur, Xingying Zhang, Qian LiuAI Agents for Scientific Discovery Materials Property Prediction
Department of Applied Computer Science, The University of Winnipeg, 515 Portage Avenue, Winnipeg, MB R3B 2E9, Canada. · Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.
UNATE: UNsupervised ATomic Embedding for crystal structures property prediction
May 25, 2026Laura Solà-Garcia, Àlex Solé, Javier Ruiz-HidalgoRepresentation Learning Denoising Autoencoders
Signal Theory and Comms. Dept. UPC-Univ. Polit`ecnica de Catalunya Barcelona, Spain
Snapshot Polarimetric Display Inverse Rendering
May 24, 2026Seokjun Choi, Yunseong Moon, Kaizhang Kang +4Inverse Rendering Materials Property Prediction
1POSTECH, South Korea · 2Meta, USA
TriForces: Augmenting Atomistic GNNs for Transferable Representations
May 20, 2026Ali Ramlaoui, Alexandre Duval, Hannah Bull +4Disentangled Representation Learning Machine Learning Interatomic Potentials
Entalpic, Paris, France · Universit´e Paris-Saclay, CentraleSup´elec, Inria, Gif-sur-Yvette, France
MatPhys: Learning Material-Aware Physics Parameters for Deformable Object Simulation from Videos
May 19, 2026Yang Yang, Yiyan Wang, Zheming Liu +1Dynamic Scene Reconstruction Materials Property Prediction
1The University of Osaka · 3Huawei Technologies Japan K.K. · 2The University of Tokyo
Topological Data Analysis combined with Machine Learning for Predicting Permeability of Porous Media
May 17, 2026Ebru Dagdelen, Catherin Neena Lalu, Aakash Karlekar +5Materials Property Prediction
Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, NJ · Department of Physics, New Jersey Institute of Technology, Newark, NJ
CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials
May 17, 2026Yanjie Li, Jian Xu, Xu-Yao Zhang +4Multimodal Large Language Models Materials Property Prediction
AnnLab, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China. · Zhongguancun Academy, Zhongguancun Academy, Beijing, China. · School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China. +3
Evolutionary Extreme Learning Machine of ab-initio Energy Landscapes for Crystal Structure Prediction using Manta Ray Optimization with Levy Flight
May 16, 2026Adrian Rubio-SolisEvolutionary Optimization Crystal Structure Prediction
Hamlyn Centre for Robotic Surgery, Imperial College London (ICL) · London SW7 2AZ, UK United Kingdom
Universal Magnetic Structure Prediction from Atomic Coordinates with Near-Experimental Accuracy
May 15, 2026Abhijatmedhi Chotrattanapituk, Ryotaro Okabe, Eunbi Rha +6Materials Property Prediction
Quantum Measurement Group, MIT, Cambridge, MA 02139, USA · Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA · Department of Chemistry, MIT, Cambridge, MA 02139, USA +4
EndoGSim: Physics-Aware 4D Dynamic Endoscopic Scene Simulations via MLLM-Guided Gaussian Splatting
May 15, 2026Changjing Liu, Yiming Huang, Long Bai +23D Gaussian Splatting Robotics Simulation
Department of Electronic Engineering, The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
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.
PhysEditBench: A Protocol-Conditioned Benchmark for Dense Physical-Map Prediction with Image Editors
May 13, 2026Jiaxin Yang, Yu Hou, Muxin Liu +5Image Editing Evaluation Depth Estimation
Southern University of Science and Technology, Shenzhen, China · The University of Hong Kong, Hong Kong, China · East China Normal University, Shanghai, China
It's All Connected: Topology-Aware Structural Graph Encoding Improves Performance on Polymer Prediction
May 11, 2026H. Ibrahim Erdogan, Punith Raviswamy, Nikita Agrawal +5Graph Representation Learning Materials Property Prediction
University of Bayreuth, Germany · Faculty of Engineering Science, University of Bayreuth, Germany · Faculty of Mathematics, Physics & Computer Science, University of Bayreuth, Germany +5
Teaching Molecular Dynamics to a Non-Autoregressive Ionic Transport Predictor
May 10, 2026Jiyeon Kim, Byungju Lee, Won-Yong ShinMaterials Property Prediction Molecular Dynamics Simulation
School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul, Republic of Korea · Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea · Nanoscience and Technology, KIST School, University of Science and Technology, Seoul, Republic of Korea
Beyond the Black Box: An Interpretable Machine Learning Framework for Predicting Electronic Structure Microdescriptors and Structure-Performance Relationships in Fe-based Catalytic Systems
May 9, 2026Oyinkansola RomiluyiMaterials Property Prediction Materials Science
University of Texas at Austin
Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction
May 8, 2026Jingzhan Ge, Ajeeth Vellore, Ajinkya Palwe +5Interpretable ML Materials Property Prediction
School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut, Storrs, CT 06269 USA · U.S. Army Combat Capabilities Development Command, Ground Vehicle Systems Center, Warren, MI 48397 USA
Can LLMs Predict Polymer Physics Just by Reading Synthesis and Processing Prose?
May 7, 2026Yuchu Liu, Rui Zhu, Jingwei Xiong +1Materials Property Prediction
Yale University · UC Davis · Indiana University Bloomington
GlazyBench: A Benchmark for Ceramic Glaze Property Prediction and Image Generation
May 7, 2026Ziyu Zhai, Siyou Li, Juexi Shao +1Conditional Image Generation Materials Property Prediction
Queen Mary University of London
Polarizable atomic multipoles for learning long-range electrostatics
May 7, 2026Yoonjae Park, Dongjin Kim, Daniel S. King +5Machine Learning Interatomic Potentials Materials Property Prediction
Department of Chemistry, UC Berkeley, California 94720, United States · Bakar Institute of Digital Materials for the Planet, UC Berkeley, California 94720, United States · Lawrence Livermore National Laboratory, Livermore, CA, USA +1
Building informative materials datasets beyond targeted objectives
May 6, 2026Rafael Espinosa Castañeda, Ashley Dale, Hongchen Wang +6Data Selection Materials Property Prediction
Department of Materials Science and Engineering, University of Toronto, Canada. · 4Vector Institute for Artificial Intelligence, Toronto, Canada. · Department of Computer Science, Princeton University +2
Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Constrained Neural Network
Apr 30, 2026Radmir Karamov, Tagir KaramovMaterials Property Prediction Materials Science
Center for Material Technologies · Skolkovo Institute of Science and Technology · Center for Petroleum Science and Engineering

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UniPixie: Unified and Probabilistic 3D Physics Learning via Flow Matching

Qilin Huang, Quynh Anh Huynh, Long Le, Chen Wang+4

University of Pennsylvania · Southern University of Science and Technology · Work done during an internship at UPenn+1

Materials Property Prediction Conditional Generative Modeling Physics-Based Simulation Deformable Object Simulation

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Existing feed-forward networks excel at predicting a single set of physical properties from visual appearance, but this point-estimate paradigm fundamentally fails to capture the real world's inherent physical ambiguity. We address this by reframing physics prediction as a task of learning a controllable, continuous distribution of material properties. We introduce UNIPIXIE, a framework trained to predict a continuous and parameterized path of physically plausible material properties from a single visual input. By learning a direct mapping along an object's softest-to-stiffest spectrum on our PIXIEMULTIVERSE dataset, UNIPIXIE allows for controllable generation of diverse, physically valid material fields via a single intuitive parameter. Crucially, UNIPIXIE introduces a novel unified architecture to produce simulation-ready parameters for diverse physics solvers, including continuum-based Material Point Method (MPM), reduced-order deformation based on Linear Blend Skinning (LBS), and anchor-based Spring-Mass systems, addressing a key portability issue in prior work. Experiments show our approach not only generates a rich variety of plausible dynamics but also reduces Young's Modulus prediction error by over 50% against the strongest deterministic baseline, bridging the gap between static point estimates and the continuous nature of physical reality. Project page: https://unipixie.github.io/

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