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Deep Learning for Polymer Discovery

By Gang Liu

Deep Learning for Polymer Discovery Computer Science

Deep Learning for Polymer Discovery

0 - Default Title
Description
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.
Product details
Number of Pages:
136
Release Date:
2025-05-24
Publication Date:
2025-05-24
Publisher:
Springer
Languages:
Original: English
ISBN10:
3031847318
ISBN13:
9783031847318
GPSR Manufacturer Reference:
[email protected]
Weight:
415 g
Height:
173 cm
Width:
246 cm
Thickness:
14 cm
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