Liu / Inae / Jiang Deep Learning for Polymer Discovery

Foundation and Advances
1. Auflage 2025
ISBN: 978-3-031-84732-5
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 123 Seiten

Reihe: Synthesis Lectures on Data Mining and Knowledge Discovery

This book presents a comprehensive range of topics in deep learning for polymer discovery, from fundamental concepts to advanced methodologies.  These topics are crucial as they address critical challenges in polymer science and engineering. With a growing demand for new materials with specific properties, traditional experimental methods for polymer discovery are becoming increasingly time-consuming and costly. Deep learning offers a promising solution by enabling rapid screening of potential polymers and accelerating the design process.  The authors begin with essential knowledge on polymer data representations and neural network architectures, then progress to deep learning frameworks for property prediction and inverse polymer design. The book then explores both sequence-based and graph-based approaches, covering various neural network types including LSTMs, GRUs, GCNs, and GINs. Advanced topics include interpretable graph deep learning with environment-based augmentation, semi-supervised techniques for addressing label imbalance, and data-centric transfer learning using diffusion models.  The book aims to solve key problems in polymer discovery, including accurate property prediction, efficient design of polymers with desired characteristics, model interpretability, handling imbalanced and limited labeled data, and leveraging unlabeled data to improve prediction accuracy.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Liu, Gang

Inae, Eric

Jiang, Meng

Inhaltsverzeichnis

Polymer Data and Deep Neural Networks.- Deep Learning for Polymer Property Prediction.- Deep Learning for Inverse Polymer Design.- Interpretable Learning: Graph Rationalization with Environment-based Augmentation.- Imbalanced Learning: Semi-Supervised Graph Imbalanced Regression.- Generative Modeling: Data-Centric Learning from Unlabeled Graphs with Diffusion Models.

Über Autor(innen)

Gang Liu is a 4th 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 ten publications in top data mining and machine learning venues, including KDD, NeurIPS, ICML, DAC, ACL, TKDE, and TKDD. His methods have contributed to the discovery of new polymers, with findings published in Cell Reports Physical Science and secured by a provisional patent. He receives the 2024-2025 IBM PhD Fellowship for his work on Foundation Models.

Eric Inae is a 3rd 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. His research emphasis is in graph machine learning with applications in material discovery and polymer science. He was awarded with the Dean’s Fellowship from the University of Notre Dame.

Meng Jiang, Ph.D., is an Associate Professor in the Department of Computer Science and Engineering at the University of Notre Dame. He received his B.E. and Ph.D. from Tsinghua University. He was a visiting scholar at Carnegie Mellon University and a postdoc at the University of Illinois Urbana-Champaign. He is interested in data mining, machine learning, and natural language processing. His data science research focuses on graph and text data for applications such as material discovery, question answering, user modeling, online education, and mental healthcare. He received the CAREER Award from the National Science Foundation and is a Senior Member of ACM and IEEE.