Deep Learning for Polymer Discovery: Foundation and Advances
暫譯: 聚合物發現的深度學習：基礎與進展

Liu, Gang, Inae, Eric, Jiang, Meng

Deep Learning for Polymer Discovery: Foundation and Advances
  • 出版商: Springer
  • 出版日期: 2025-05-24
  • 售價: $1,850
  • 貴賓價: 9.5 折 $1,758
  • 語言: 英文
  • 頁數: 123
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031847318
  • ISBN-13: 9783031847318
  • 相關分類: DeepLearning

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

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.

商品描述(中文翻譯)

這本書全面介紹了深度學習在聚合物發現中的各種主題，從基本概念到進階方法論。這些主題對於聚合物科學和工程中的關鍵挑戰至關重要。隨著對具有特定性質的新材料需求的增加，傳統的聚合物發現實驗方法變得越來越耗時且成本高昂。深度學習提供了一個有前景的解決方案，能夠快速篩選潛在的聚合物並加速設計過程。作者首先介紹聚合物數據表示和神經網絡架構的基本知識，然後進一步探討用於性質預測和逆聚合物設計的深度學習框架。接著，書中探討了基於序列和基於圖形的方法，涵蓋了各種神經網絡類型，包括 LSTMs、GRUs、GCNs 和 GINs。進階主題包括具有環境增強的可解釋圖形深度學習、針對標籤不平衡的半監督技術，以及使用擴散模型的數據中心轉移學習。這本書旨在解決聚合物發現中的關鍵問題，包括準確的性質預測、有效設計具有所需特性的聚合物、模型可解釋性、處理不平衡和有限標記數據，以及利用未標記數據來提高預測準確性。

此外，這本書：
- 包含示例和實驗，以展示這些方法在現實世界聚合物數據集上的有效性
- 提供詳細的問題定義、方法描述和實驗結果
- 作為尋求在材料研究和開發中利用人工智慧的讀者的參考
- 包含示例和實驗，以展示這些方法在現實世界聚合物數據集上的有效性

Gang Liu 是聖母大學計算機科學與工程系的四年級博士生。

Eric Inae 是聖母大學計算機科學與工程系的三年級博士生。

Meng Jiang 博士是聖母大學計算機科學與工程系的副教授。

作者簡介

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.

作者簡介(中文翻譯)

Gang Liu 是聖母大學計算機科學與工程系的四年級博士生。他的研究專注於聚合物材料發現的圖形和生成學習。他在頂尖的數據挖掘和機器學習會議上發表了十多篇論文，包括 KDD、NeurIPS、ICML、DAC、ACL、TKDE 和 TKDD。他的方法促進了新聚合物的發現，相關研究成果已發表於《Cell Reports Physical Science》，並獲得了臨時專利。他因其在基礎模型方面的研究獲得了 2024-2025 年 IBM 博士生獎學金。

Eric Inae 是聖母大學計算機科學與工程系的三年級博士生。他於 2022 年獲得安德魯斯大學的計算機科學學士學位和數學學士學位。他的研究重點是圖形機器學習，應用於材料發現和聚合物科學。他獲得了聖母大學的院長獎學金。

孟江（Meng Jiang），博士，是聖母大學計算機科學與工程系的副教授。他在清華大學獲得了工程學士和博士學位。他曾是卡內基梅隆大學的訪問學者，並在伊利諾伊大學香檳分校擔任博士後研究員。他對數據挖掘、機器學習和自然語言處理感興趣。他的數據科學研究專注於圖形和文本數據，應用於材料發現、問題回答、用戶建模、在線教育和心理健康護理等領域。他獲得了國家科學基金會的 CAREER 獎，並是 ACM 和 IEEE 的資深會員。

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