Contact
How to shop
Delivery
Shopping guide
English
107 b
Up to 30 days for returns
Customers also purchased
/
Paperback
7 526
Ft
/
Paperback
4 437
Ft
/
Paperback
6 999
Ft
/
Paperback
25 504
Ft
/
Paperback
10 652
Ft
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.
About the book
English
Give this book today
It's easy
1 Add to cart and choose Deliver as present at the checkout 2 We'll send you a voucher 3 The book will arrive at the recipient's addressYou might also be interested in
/
Hardback
8 968
Ft
/
Paperback
10 819
Ft
/
Paperback
11 677
Ft
/
Hardback
20 958
Ft
