Polymer informatics tools have been recently gaining ground to efficiently and effectively develop, design, and discover new polymers that meet specific application needs. So far, however, these data-driven efforts have largely focused on homopolymers. Here, we address the property prediction challenge for copolymers, extending the polymer informatics framework beyond homopolymers. Advanced polymer fingerprinting and deep-learning schemes that incorporate multi-task learning and meta-learning are proposed. A large data set containing over 18,000 data points of glass transition, melting, and degradation temperature of homopolymers and copolymers of up to two monomers is used to demonstrate the copolymer prediction efficacy. The developed models are accurate, fast, flexible, and scalable to more copolymer properties when suitable data become available.
Pathway to a fully data-driven geotechnics: lessons from materials informatics
Research Organization of Information and Systems（信息与系统研究机构）；The Institute of Statistical Mathematics（统计数学研究所）；The Graduate University for Advanced Studies（综合研究大学院大学）；Tohoku University（东北大学）；Kyoto University（京都大学）
Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informatics
Leverhulme Research Centre for Functional Materials Design（Leverhulme功能材料设计研究中心）；University of Liverpool（利物浦大学）；Department of Computer Science（计算机科学系）；Department of Chemistry（化学系）
本文提出将 Deep InfoMax 作为材料信息学的自监督学习框架，通过最大化晶体点集表示与向量表示间的互信息，在无性质标签的大规模晶体数据上预训练 Site-Net 模型，从而提升小数据量下带隙和形成能预测的性能。
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.
Autonomous Multi-Agent AI for High-Throughput Polymer Informatics: From Property Prediction to Generative Design Across Synthetic and Bio-Polymers
Institute of Biomedical Engineering University of Oxford（生物医学工程研究所牛津大学）；Department of Mechanical and Aerospace Engineering University of Missouri–Columbia（机械与航空航天工程系密苏里大学-哥伦比亚分校）；Center for Engineering, Modeling and Applied Social Sciences Federal University of ABC (UFABC)（工程、建模与应用社会科学中心巴西联邦大学ABC分校）
High-dimensional Bayesian Optimization of Hyperparameters for an Attention-based Network to Predict Materials Property: a Case Study on CrabNet using Ax and SAASBO
Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes
a Department of Materials Science；Engineering, Texas A\&M University, College Station, TX, USA 77843；b J. Mike Walker '66 Department of Mechanical Engineering, Texas A\&M University, College Station, TX, USA 77843；c Wm Michael Barnes '64 Department of Industrial；Systems Engineering, Texas A\&M University, College Station, TX, USA 77843；d Max-Planck-Institute for Sustainable Materials, D\"usseldorf, Germany, 40237；e Theoretical Division T-1, Los Alamos National Laboratory, Los Alamos, NM, USA 87544
Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability
Laboratory of Applied Materials and Interfaces, Federal University of Rio Grande do Sul（南里奥格兰德联邦大学应用材料与界面实验室）；Institute of Condensed Matter and Nanosciences, Université Catholique de Louvain（鲁汶天主教大学凝聚态与纳米科学研究所）；Ruhr-Universität Bochum（波鸿鲁尔大学）
