Modeling Polymers with Neural Networks

Eric Inae · Yuhan Liu · Yihan Zhu · Jiaxin Xu · Gang Liu · Renzheng Zhang · Tengfei Luo · Meng Jiang

2025年7月 · ACS In Focus 第 1 本图书 · American Chemical Society

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This primer explains at a fundamental level how machine learning models are created, trained, and evaluated while focusing specifically on applications in polymer informatics. The authors introduce techniques suited to polymer data, providing a foundational understanding that the reader can use as a launchpad for more advanced methods. Additionally, to serve as an interactive aid for learning within this primer, the authors have also provided tutorials at the end of each chapter. With this additional tool, the reader will be well-equipped to begin implementing these machine learning models independently and contribute to the research literature surrounding the uses of neural networks within polymer informatics.

The authors wrote this primer to inspire future engineers and scientists to think creatively about blending computational techniques with empirical engineering insights. This primer is useful for applying machine learning to material sciences and exploring novel polymer composites for next-generation aerospace applications.

作者简介

Eric Inae is a third-year Ph.D. student in the Department of Computer Science and Engineering at the University of Notre Dame. He received his B.S. in Computer Science and B.S. in Mathematics from Andrews University in 2022. In the same year, he was awarded the Dean’s Fellowship from the University of Notre Dame. His research emphasizes graph machine learning and graph self-supervised learning with applications in materials science and polymer informatics.

Yuhan Liu is a second-year Ph.D. student in Chemistry at the University of Notre Dame. She received her M.Sc. degree in Chemical Sciences from the National University of Singapore in 2021 and her B.S. in Chemistry from Southwest University in 2020. Her research focuses on polymer informatics, integrating machine learning and molecular dynamics simulations to accelerate polymer discovery and design. She is particularly interested in identifying functional polymeric materials for heat transfer and mechanical applications.

Yihan Zhu is a first-year Ph.D. student in the Department of Computer Science and Engineering at the University of Notre Dame. She received her Master of Science degree in Electrical Engineering from Columbia University in 2023 as an honor student and received the Graduation Excellent Award. She specializes in graph machine learning, focusing on materials and polymer science applications.

Jiaxin Xu is a fifth-year Ph.D. student in the Department of Aerospace and Mechanical Engineering at the University of Notre Dame under the supervision of Professor Tengfei Luo. He received his Bachelor of Engineering in Energy and Power Engineering from the Huazhong University of Science and Technology, China, in 2020. His research focuses on polymer informatics, high-throughput computational simulation, and nanoscale heat and mass transport. He has published over 10 peer-reviewed papers in top machine learning and material discovery venues.

Gang Liu is a fourth-year Ph.D. student in the Department of Computer Science and Engineering at the University of Notre Dame. His research focuses on graph and generative learning for polymeric material discovery. He has over 10 publications in top data mining and machine learning venues, including KDD, NeurIPS, ICLR, ICML, DAC, ACL, TKDE, and TKDD. His methods have contributed to discovering new polymers, with findings published in Cell Reports Physical Science and secured by a provisional patent. He received the 2024–2025 IBM PhD Fellowship for his work on Foundation Models.

Renzheng Zhang is a third-year Ph.D. student in the Department of Aerospace and Mechanical Engineering at the University of Notre Dame. He received his Bachelor of Engineering in Civil Engineering from the Harbin Institute of Technology, China, in 2022. His research focuses on polymer informatics, high-throughput computational simulation, and machine learning-driven materials discovery.

Tengfei Luo is the Dorini Family Professor and Associate Chair in the Department of Aerospace and Mechanical Engineering at the University of Notre Dame. He is a Fellow of the Lucy Family Institute for Data and Society. His research focuses on polymer physics and uses multiscale modeling techniques, from first-principles calculations and molecular simulations to mesoscale models. He has been working on polymer informatics, focusing on thermal transport properties. He has published more than 180 journal articles. He is a Fellow of the American Society of Mechanical Engineers.

Meng Jiang is an associate professor in the Department of Computer Science and Engineering at the University of Notre Dame. He is appointed as the Director of the Foundation Model Lab and Fellow at the Lucy Family Institute for Data and Society. His research fields are data mining, machine learning, and artificial intelligence. His data science research focuses on graph and text data for applications such as material discovery, recommender systems, question answering, education, and mental health. He has published over 180 peer-reviewed articles in international conferences and top journals and received multiple best paper awards. He has organized 10 workshops and offered 14 tutorials at international conferences such as KDD, ACL, EMNLP, and AAAI. He is a Senior Member of the IEEE and ACM.