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.

There have been many new developments since the first edition of this book was published back in 2015. These can be summarized as follows: integration of multiple properties into self-healing polymer materials, such as the shape memory effect and flame retardancy; beyond self-healing and the development of recyclable thermoset polymers; and the application of self-healing polymers in both 3D and 4D printing. Recent Advances in Smart Self-healing Polymers and Composites, Second Edition provides a comprehensive introduction to the fascinating field of smart self-healing polymers and composites. All chapters are brought fully-up-to-date with the addition of six brand new contributions on the characterization of self-healing polymers, light-triggered self-healing, additive manufacturing, multifunctional thermoset polymers with self-healing ability, and recyclable thermoset polymers and 4D printing. It is written for a large readership including not only R&D researchers from diverse backgrounds such as chemistry, materials science, aerospace, physics, and biological science, but also for graduate student working on self-healing technologies as well as their newly developed applications. - Features new chapters on characterization of self-healing polymers, light-triggered self-healing, additive manufacturing, multifunctional thermoset polymers with self-healing ability, recyclable thermoset polymers and 4D printing - All chapters have been significantly updated from the previous edition - Provides a grounding in all key areas of research to bring people up to speed with the latest developments

Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.

Taking an interdisciplinary approach, this new book considers state-of-the-art developments and research in polymer science, such as advanced polymers, composites and nanocomposites, and the role of polymers in the progress of green chemistry and medicine. Polymers are studied in fields as diverse as polymer science (polymer chemistry and polymer physics), biophysics, biochemistry, and more generally in materials science and engineering. Polymer matrix composites (PMCs) and nanocomposites (PMNCs) are widely used in hightech material structures such as in the automotive, marine, and aerospace industries. Their impact on the physical and mechanical performance is mainly due to their reinforcing agents, fibers (glass, carbon, aramid) or nanofibers (MMT, CNTs, graphene, etc) but also due to a perfect mastery of the matrix/reinforcement interface.

This book presents innovative research works to automate, innovate, design, and deploy AI fo real-world applications. It discusses AI applications in major cutting-edge technologies and details about deployment solutions for different applications for sustainable development. The application of Blockchain techniques illustrates the ways of optimisation algorithms in this book. The challenges associated with AI deployment are also discussed in detail, and edge computing with machine learning solutions is explained. This book provides multi-domain applications of AI to the readers to help find innovative methods towards the business, sustainability, and customer outreach paradigms in the AI domain. • Focuses on virtual machine placement and migration techniques for cloud data centres • Presents the role of machine learning and meta-heuristic approaches for optimisation in cloud computing services • Includes application of placement techniques for quality of service, performance, and reliability improvement • Explores data centre resource management, load balancing and orchestration using machine learning techniques • Analyses dynamic and scalable resource scheduling with a focus on resource management The reference work is for postgraduate students, professionals, and academic researchers in computer science and information technology.

This is truly an exciting time to be in the ?eld of polymer science. Advances in polymerization methods are providing polymer scientists with the ability to specify and control polymer composition, structure, architecture, and molecular weight to a degree that was not possible just a decade ago. This, in turn, is resulting in many novel application possibilities of polymers ranging from drug delivery systems and nanolithographyto stimuli-responsivematerials and many others. In addition,many of the application areas of polymers – such as coatings, adhesives, thermoplastics, composites, and personal care – are also taking advantage of the ability to design polymersduringtheir developmentefforts. Not to forget,manyof these applications of polymers involve mixing polymers with solvents, catalysts, colorants, and many other ingredients to prepare a formulated product. However, the tuning of polymer composition and structure as well as polymer formulations to optimize the ?nal performance properties can be challenging, - pecially since in many cases several interacting variables need to be optimized simultaneously. This is where the methodologies and techniques of combinatorial and high-throughput experimentation to synthesize and characterize polymer - braries can be an invaluable approach. Simply put, a polymer library is a collection of multiple polymer samples having a systematic variation in one or more variables related to composition, structure, or process. Various methods and strategies have been explored to ef?ciently prepare a large number of polymer samples and also to screen these samples for key properties of interest.

This three volume set (CCIS 853-855) constitutes the proceedings of the 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2017, held in Cádiz, Spain, in June 2018. The 193 revised full papers were carefully reviewed and selected from 383 submissions. The papers are organized in topical sections on advances on explainable artificial intelligence; aggregation operators, fuzzy metrics and applications; belief function theory and its applications; current techniques to model, process and describe time series; discrete models and computational intelligence; formal concept analysis and uncertainty; fuzzy implication functions; fuzzy logic and artificial intelligence problems; fuzzy mathematical analysis and applications; fuzzy methods in data mining and knowledge discovery; fuzzy transforms: theory and applications to data analysis and image processing; imprecise probabilities: foundations and applications; mathematical fuzzy logic, mathematical morphology; measures of comparison and entropies for fuzzy sets and their extensions; new trends in data aggregation; pre-aggregation functions and generalized forms of monotonicity; rough and fuzzy similarity modelling tools; soft computing for decision making in uncertainty; soft computing in information retrieval and sentiment analysis; tri-partitions and uncertainty; decision making modeling and applications; logical methods in mining knowledge from big data; metaheuristics and machine learning; optimization models for modern analytics; uncertainty in medicine; uncertainty in Video/Image Processing (UVIP).

This book describes the forcefields/interatomic potentials that are used in the atomistic-scale and molecular dynamics simulations. It covers mechanisms, salient features, formulations, important aspects and case studies of various forcefields utilized for characterizing various materials (such as nuclear materials and nanomaterials) and applications. This book gives many help to students and researchers who are studying the forcefield potentials and introduces various applications of atomistic-scale simulations to professors who are researching molecular dynamics.