Machine Learning For Polymer Informatics
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Machine Learning for Polymer Informatics
Machine learning has significantly accelerated the development of new polymer materials. Machine Learning for Polymer Informatics introduces the reader to the most popular ways of applying machine learning in polymer informatics. This primer will equip the reader to ask the right questions about the application of machine learning in their areas of interest, as well as critically interpret publications leveraging machine learning methods. The authors encourage readers to try machine learning techniques when they have sufficient data in their area of interest. The development of machine learning has far exceeded human imagination, and with sufficient data, everything is full of possibilities.
Advanced Machine Learning with Evolutionary and Metaheuristic Techniques
This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of machine learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency. Or unlock the strategies behind hyperparameter optimization to enhance your transfer learning algorithms, yielding remarkable outcomes. Or embark on an illuminating journey through evolutionary techniques designed for constructing deep-learning frameworks. The book also introduces an intelligent RPL attack detection system tailored for IoT networks. Explore a promising avenue of optimization by fusing Particle Swarm Optimization with Reinforcement Learning. It uncovers the indispensable role of metaheuristics in supervised machine learning algorithms. Ultimately, this book bridges the realms of evolutionary dynamic optimization andmachine learning, paving the way for pioneering innovations in the field.
Materials Informatics III
This contributed volume focuses on the application of machine learning and cheminformatics in predictive modeling for organic materials, polymers, solvents, and energetic materials. It provides an in-depth look at how machine learning is utilized to predict key properties of polymers, deep eutectic solvents, and ionic liquids, as well as to improve safety and performance in the study of energetic and reactive materials. With chapters covering polymer informatics, quantitative structure–property relationship (QSPR) modeling, and computational approaches, the book serves as a comprehensive resource for researchers applying predictive modeling techniques to advance materials science and improve material safety and performance.