Deep Learning for Polymer Discovery

Beschreibung

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. In addition, this book: Includes examples and experiments to demonstrate the effectiveness of the methods on real-world polymer datasets Offers detailed problem definitions, method descriptions, and experimental results Serves as a reference for readers seeking to leverage artificial intelligence in materials research and development < Offers detailed problem definitions and method descriptions Includes examples and experiments to demonstrate the effectiveness of the methods on real-world polymer datasets Gang Liu is a 4th year Ph.D. student in the Department of Computer Science and Engineering at the University of Notre Dame.  Eric Inae is a 3rd year Ph.D. student in the Department of Computer Science and Engineering at 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.

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.

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Gang Liu

Themen in »Deep Learning for Polymer Discovery«

Deep Learning for Polymer Discovery AI in Materials Science and Engineering Neural Networks for Polymer Property Prediction Inverse Polymer Design using Machine Learning Graph Neural Networks in Polymer Science Interpretable AI for Materials Research Semi-supervised Learning in Polymer Discovery Data-centric Transfer Learning for Polymers Machine Learning for Sustainable Materials Development Advanced Polymer Design Techniques using Artificial Intelligence

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