Prediction of material property is a key problem because of its significance to material design and screening. We present a brand-new and general machine learning method for material property prediction. As a representative example, polymer compatibility is chosen to demonstrate the effectiveness of our method. Specifically, we mine data from related literature to build a specific database and give a prediction based on the basic molecular structures of blending polymers and, as auxiliary, the blending composition. Our model obtains at least 75% accuracy on the dataset consisting of thousands of entries. We demonstrate that the relationship between structure and properties can be learned and simulated by machine learning method.
PolymerGPT: Multi-property Optimization with a Decoder-Based GPT Model for Generative Polymer Design
Texas A&M University（得克萨斯农工大学）；University of Pittsburgh（匹兹堡大学）；Virginia Tech（弗吉尼亚理工大学）；University of North Carolina Greensboro（北卡罗来纳大学格林斯伯勒分校）；Old Dominion University（奥多明尼昂大学）
PolyMon: A Unified Framework for Polymer Property Prediction
Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, White City Campus, Wood Lane, London, W12 0BZ, U.K.（化学系，分子科学研究中心，伦敦帝国理工学院，白城校区，Wood Lane，伦敦，W12 0BZ，英国）
Multimodal machine learning with large language embedding model for polymer property prediction
Department of Materials Science and Engineering, University of Delaware, Newark, Delaware 19716, United States（材料科学与工程系，德雷克塞尔大学）；Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States（化学系，宾夕法尼亚大学）
PolyFusionAgent: A Multimodal Foundation Model and Autonomous AI Assistant for Polymer Property Prediction and Inverse Design
Department of Applied Computer Science, The University of Winnipeg（应用计算机科学系，温尼伯大学）；Department of Mechanical Engineering, University of Manitoba（机械工程系，曼尼托巴大学）
MIPS: a Multimodal Infinite Polymer Sequence Pre-training Framework for Polymer Property Prediction
Department of Electronic Engineering, Tsinghua University Beijing China；Zhongguancun Institute of Artificial Intelligence Beijing China；Department of Electronic Engineering \& College of AI, Tsinghua University Beijing National Research Center for Information Science；Department of Electronic Engineering, Tsinghua University；Zhongguancun Institute of Artificial Intelligence
Gaussian Process Regression-based Knowledge Distillation Framework for Simultaneous Prediction of Physical and Mechanical Properties of Epoxy Polymers
Sindu B.S Special and Multifunctional Structures Laboratory, CSIR-Structural Engineering Research Centre, Taramani, Chennai, Tamil Nadu, India（Sindu B.S 特殊和多功能结构实验室，CSIR-结构工程研究中心，坦马尼，钦奈，泰米尔纳德邦，印度）；Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Schloss Birlinghoven, 53757 Sankt Augustin , Germany（弗劳恩霍夫算法与科学计算研究所SCAI，伯灵霍芬城堡，53757圣奥古斯滕，德国）
Reducing Data Requirements for Sequence-Property Prediction in Copolymer Compatibilizers via Deep Neural Network Tuning
Bellini College of Artificial Intelligence, Cybersecurity \& Computing, University of South Florida, Tampa, Florida, U.S.A.；Department of Chemical, Biological；Materials Engineering, University of South Florida, Tampa, Florida, U.S.A.
