ACS Symposium Series

Bede Pittenger, Cheng Qiu, Jinglei Yang, Alejandro E. Rodríguez-Sánchez, Aditya Sonpal, Mohammad Atif Faiz Afzal, Yuling An, Anand Chandrasekaran, Mathew D. Halls, Jon Paul Janet, Shinichi Ookawara, Tomoki Yasuda, Yosuke Matsuda, Shiro Yoshikawa, Hideyuki Matsumoto, Wei-Chih Chen, Da Yan, Cheng-Chien Chen, Keisuke Ta, Yuling An

• Publisher: American Chemical Society
• Year: 2022
• Print ISBN:
  9780841297623
• eBook ISBN:
  9780841297623
• Topic: Physical Sciences
• Subtopic: Chemistry
• Index: Chemistry

Table of contents

• Machine Learning in Materials Informatics: Methods and Applications
• ACS Symposium Series1416
• Foreword
• Preface
• Machine Learning Approaches for Accelerating the Discovery of Thermoelectric Materials
• Machine Learning for the Design of Novel OLED Materials
• Deep Learning to Predict Structure-Property Relationships of Polymer Blends
• Machine Learning Applications in Composites: Manufacturing, Design, and Characterization
• Modeling Nonlinear Compressive Stress Responses in Closed-Cell Polymer Foams Using Artificial Neural Networks: A Comprehensive Case Study
• Benchmarking Machine Learning Descriptors for Crystals
• Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks
• Machine Learning in Porous Materials: SVM-Based Characterization and CGAN-Driven Materials Discovery and Design
• Machine Learning and First-Principles Discovery of Ternary Superhard Materials
• Data in Materials and Catalysts Informatics
• Editor’s Biography
• Indexes
• Author Index
• Subject Index
• Preface
• Machine Learning Approaches for Accelerating the Discovery of Thermoelectric Materials
• Machine Learning for the Design of Novel OLED Materials
• Deep Learning to Predict Structure-Property Relationships of Polymer Blends
• Machine Learning Applications in Composites: Manufacturing, Design, and Characterization
• Modeling Nonlinear Compressive Stress Responses in Closed-Cell Polymer Foams Using Artificial Neural Networks: A Comprehensive Case Study
• Benchmarking Machine Learning Descriptors for Crystals
• Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks
• Machine Learning in Porous Materials: SVM-Based Characterization and CGAN-Driven Materials Discovery and Design
• Machine Learning and First-Principles Discovery of Ternary Superhard Materials
• Data in Materials and Catalysts Informatics
• Editor’s Biography
• Indexes
• Author Index
• Subject Index