Polymer Property Prediction

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13 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

An open benchmark for machine learning-based polymer property prediction
Sep 22, 2026Robert W. Learsch, Nicholas Liesen, Daniel S. Levine +2Materials Property Prediction Polymer Property Prediction
Lawrence Livermore National Laboratory · FAIR at Meta
HiPoly: a hierarchical polymer-native AI framework for property prediction and generative design
Sep 2, 2026Ge Sun, Gervasio Zaldivar, Yuan Tian +5Hierarchical GNNs Polymer Property Prediction
Department of Chemical and Biomolecular Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, USA · Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, USA · Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA +3
UniPolymer: A Unified Framework for Property Prediction, Structure Recommendation, and Evaluation in Polyimide Design
Jul 31, 2026Junquan Hu, Zhihui Wang, Peng Xu +4Materials Science Molecular Property Prediction
School of Software Dalian University of Technology Dalian, China · Department of Information Engineering The Chinese University of Hong Kong Hong Kong SAR, China
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
Periodic Topological Deep Learning for Polymer Design and Discovery
May 26, 2026Yasharth Yadav, Tze Kwang Gerald Er, Atsushi Goto +1Materials Property Prediction Polymer Property Prediction
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.
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
Can LLMs Predict Polymer Physics Just by Reading Synthesis and Processing Prose?
May 7, 2026Yuchu Liu, Rui Zhu, Jingwei Xiong +1Materials Property Prediction Polymer Property Prediction
Yale University · UC Davis · Indiana University Bloomington
PolyJarvis: An LLM-Orchestrated Agent for Automated All-Atom Molecular Dynamics of Amorphous Homopolymers
Apr 2, 2026Alexander Zhao, Achuth Chandrasekhar, Amir Barati FarimaniLLM Agents Materials 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
Rapid Neural Network Prediction of Linear Block Copolymer Free Energies
Mar 18, 2026Ian Chen, Alfredo Alexander-KatzFree Energy Calculation Polymer Property Prediction
Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge, MA 02139
Intelligent Materials Modelling: Large Language Models Versus Partial Least Squares Regression for Predicting Polysulfone Membrane Mechanical Performance
Mar 14, 2026Dingding Cao, Mieow Kee Chan, Wan Sieng Yeo +2Materials Property Prediction Polymer Property Prediction
School of Big Data, Baoshan University, Baoshan, 678000, Yunnan Province, China. · Centre for Water Research, Faculty of Engineering, Built Environment and Information Technology, SEGi University. Jalan Teknologi, Kota Damansara, 47810 Petaling Jaya, Selangor Darul Ehsan, Malaysia. · School of Energy and Chemical Engineering, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900 Sepang, Selangor, Malaysia. +3
Gaussian Process Regression-based Knowledge Distillation Framework for Simultaneous Prediction of Physical and Mechanical Properties of Epoxy Polymers
Mar 12, 2026Sindu B. S., Jan HamaekersMaterials Property Prediction Polymer Property Prediction
Special and Multifunctional Structures Laboratory, CSIR-Structural Engineering Research Centre, Taramani, Chennai, Tamil Nadu, India - 600113. · Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Schloss Birlinghoven, 53757 Sankt Augustin, Germany
PolyMon: A Unified Framework for Polymer Property Prediction
Mar 2, 2026Gaopeng Ren, Yijie Yang, Jiajun Zhou +1Polymer Property Prediction Materials Property Prediction
Department of Chemistry, Imperial College London

Sep 22, 2026J/K move · Enter open · S save

An open benchmark for machine learning-based polymer property prediction

Robert W. Learsch, Nicholas Liesen, Daniel S. Levine, Anna M. Hiszpanski+1

Lawrence Livermore National Laboratory · FAIR at Meta

Materials Property Prediction Polymer Property Prediction

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Polymer property prediction lacks open, standardized benchmarks that enable rigorous comparison of machine-learning methods, with existing resources covering only a narrow fraction of polymer architectures, such as homopolymers. We introduce Polymer Benchmark 2026 (PolyBench26), an open dataset comprising nearly 250,000 polymer-property datapoints across eight physical properties, including data from experimental measurements, density functional theory, and molecular dynamics. The benchmark supports four evaluation tasks across homopolymers and alternating, random, and block copolymers: in-distribution property prediction, dataset-size scaling, repeat-unit complexity, and transfer to held-out polymer architectures. We compare language model, graph-based, and descriptor-based approaches and find graph-based models provide the lowest errors in property prediction, retain their advantage across the evaluated training-set sizes, and remain robust to increasing repeat-unit complexity. PolyBench26 provides a reproducible foundation for developing models for the increasingly complex polymer design space. The PolyBench26 benchmark is available open-source at https://github.com/rlearsch/PolymerBenchmark2026.