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This book addresses the current status, challenges and future directions of data-driven materials discovery and design. It presents the analysis and learning from data as a key theme in many science and cyber related applications. The challenging open questions as well as future directions in the application of data science to materials problems are sketched. Computational and experimental facilities today generate vast amounts of data at an unprecedented rate. The book gives guidance to discover new knowledge that enables materials innovation to address grand challenges in energy, environment and security, the clearer link needed between the data from these facilities and the theory and underlying science. The role of inference and optimization methods in distilling the data and constraining predictions using insights and results from theory is key to achieving the desired goals of real time analysis and feedback. Thus, the importance of this book lies in emphasizing that the full value of knowledge driven discovery using data can only be realized by integrating statistical and information sciences with materials science, which is increasingly dependent on high throughput and large scale computational and experimental data gathering efforts. This is especially the case as we enter a new era of big data in materials science with the planning of future experimental facilities such as the Linac Coherent Light Source at Stanford (LCLS-II), the European X-ray Free Electron Laser (EXFEL) and MaRIE (Matter Radiation in Extremes), the signature concept facility from Los Alamos National Laboratory. These facilities are expected to generate hundreds of terabytes to several petabytes of in situ spatially and temporally resolved data per sample. The questions that then arise include how we can learn from the data to accelerate the processing and analysis of reconstructed microstructure, rapidly map spatially resolved properties from high throughput data, devise diagnostics for pattern detection, and guide experiments towards desired targeted properties. The authors are an interdisciplinary group of leading experts who bring the excitement of the nascent and rapidly emerging field of materials informatics to the reader.
This book deals with an information-driven approach to plan materials discovery and design, iterative learning. The authors present contrasting but complementary approaches, such as those based on high throughput calculations, combinatorial experiments or data driven discovery, together with machine-learning methods. Similarly, statistical methods successfully applied in other fields, such as biosciences, are presented. The content spans from materials science to information science to reflect the cross-disciplinary nature of the field. A perspective is presented that offers a paradigm (codesign loop for materials design) to involve iteratively learning from experiments and calculations to develop materials with optimum properties. Such a loop requires the elements of incorporating domain materials knowledge, a database of descriptors (the genes), a surrogate or statistical model developed to predict a given property with uncertainties, performing adaptive experimental design to guide the next experiment or calculation and aspects of high throughput calculations as well as experiments. The book is about manufacturing with the aim to halving the time to discover and design new materials. Accelerating discovery relies on using large databases, computation, and mathematics in the material sciences in a manner similar to the way used to in the Human Genome Initiative. Novel approaches are therefore called to explore the enormous phase space presented by complex materials and processes. To achieve the desired performance gains, a predictive capability is needed to guide experiments and computations in the most fruitful directions by reducing not successful trials. Despite advances in computation and experimental techniques, generating vast arrays of data; without a clear way of linkage to models, the full value of data driven discovery cannot be realized. Hence, along with experimental, theoretical and computational materials science, we need to add a “fourth leg’’ to our toolkit to make the “Materials Genome'' a reality, the science of Materials Informatics.
The book is concerned with the use of Artificial Intelligence in the discovery, production and application of new engineering materials. Topics covered include nano-robots. data mining, solar energy systems, materials genomics, polymer manufacturing, and energy conversion issues. Keywords: Artificial Intelligence, Mathematical Models, Machine Learning, Artificial Neural Networks, Bayesian Analysis, Vector Machines, Heuristics, Crystal Structure, Component Prediction, Process Optimization, Density Functional Theory, Monitoring, Classification, Nano-Robots, Data Mining, Solar Photovoltaics, Renewable Energy Systems, Alternative Energy Sources, Material Genomics, Polymer Manufacturing, Energy Conversion.
Zusammenfassung: Focusing on the fundamentals of machine learning, this book covers broad areas of data-driven modeling, ranging from simple regression to advanced machine learning and optimization methods for applications in materials modeling and discovery. The book explains complex mathematical concepts in a lucid manner to ensure that readers from different materials domains are able to use these techniques successfully. A unique feature of this book is its hands-on aspect--each method presented herein is accompanied by a code that implements the method in open-source platforms such as Python. This book is thus aimed at graduate students, researchers, and engineers to enable the use of data-driven methods for understanding and accelerating the discovery of novel materials
This volume looks at modern approaches to catalysis and reviews the extensive literature. Chapters highlight application of 2D materials in biomass conversion catalysis, plasmonic photocatalysis, catalytic demonstration of mesoporosity in the hierarchical zeolite and the effect of surface phase oxides on supported metals and catalysis. Looking to the future a chapter on ab initio machine learning for accelerating catalytic materials discovery is included. Appealing broadly to researchers in academia and industry, these illustrative chapters bridge the gap from academic studies in the laboratory to practical applications in industry not only for catalysis field but also for environmental protection. Other chapters with an industrial perspective include heterogeneous and homogeneous catalytic routes for vinyl acetate synthesis, catalysis for production of jet fuel from renewable sources by HDO/HDC and microwave-assisted catalysis for fuel conversion. Chemical reactions in ball mills is also explored. The book will be of great benefit to any researcher wanting a succinct reference on developments in this area now and looking to the future.
The book “Advances in Information and Communication Networks - Proceedings of the 2022 Future of Information and Communication Conference (FICC)” aims in presenting the latest research advances, sharing expert knowledge and exchanging ideas with the common goal of shaping the future of Information and Communication. The conference attracted 402 submissions, of which, 131 submissions (including six poster papers) have been selected through a double-blind review process by an international panel of expert referees. This book discusses on aspects of Communication, Data Science, Ambient Intelligence, Networking, Computing, Security and Internet of Things, from classical to intelligent scope. The intention is to help academic pioneering researchers, scientists, industrial engineers, and students become familiar with and stay abreast of the ever-changing technology surrounding their industry. We hope that readers find the volume interesting and valuable; it gathers chapters addressing state-of-the-art intelligent methods and techniques for solving real world problems along with a vision of the future research.
The use of artificial intelligence (AI) in various fields is of major importance to improve the use of resourses and time. This book provides an analysis of how AI is used in both the medical field and beyond. Topics that will be covered are bioinformatics, biostatistics, dentistry, diagnosis and prognosis, smart materials, and drug discovery as they intersect with AI. Also, an outlook of the future of an AI-assisted society will be explored.
This book addresses issues pertinent to mechanics and stress generation, especially in recent advanced cases of technology developments, spanning from micrometer interconnects in solar photovoltaics (PV), next-gen energy storage devices to multilayers of nano-scale composites enabling novel stretchable/flexible conductor technologies. In these cases, the mechanics of materials have been pushed to the extreme edges of human knowledge to enable cutting-edge, unprecedented functionalities and technological innovations. Synchrotron X-ray diffraction, in situ small-scale mechanical testing combined with physics-based computational modeling/simulation, has been widely used approaches to probe these mechanics of the materials at their extreme limits due to their recently discovered distinct advantages. The techniques discussed in this manuscript are highlights specially curated from the broad body of work recently reported in the literature, especially ones that the author had led the pursuits at the frontier himself. Extreme stress generation in these advanced material leads to often new failure modes, and hence, the reliability of the final product is directly affected. From the recent topics and various advanced case studies covered in this book, the reader gets an updated knowledge of how new mechanics can and has been applied in Design-for-Reliability (DfR) for some of the latest technological innovations known in our modern world. Further, this also helps in building better designs, which may avoid the pitfalls of the current practiced trends.
Uncertainty Quantification in Multiscale Materials Modeling provides a complete overview of uncertainty quantification (UQ) in computational materials science. It provides practical tools and methods along with examples of their application to problems in materials modeling. UQ methods are applied to various multiscale models ranging from the nanoscale to macroscale. This book presents a thorough synthesis of the state-of-the-art in UQ methods for materials modeling, including Bayesian inference, surrogate modeling, random fields, interval analysis, and sensitivity analysis, providing insight into the unique characteristics of models framed at each scale, as well as common issues in modeling across scales.
As a low carbon energy source, nuclear energy plays a reinforced role in a sustainable electricity mix. However, strengthening the share of nuclear energy implies the guarantee of safe, long-term operation of current systems and potentially the fostering of new constructions. Service life extension – as well as the design of future nuclear power plants – relies on the availability of robust and qualified structural materials, and their manufacturing processes. The science and engineering of materials are key in selecting robust material solutions and predicting aging mechanisms. Materials and Processes for Nuclear Energy Today and in the Future reviews different reactor concepts and fuel management systems. Nuclear equipment has to maintain integrity under extreme conditions, such as high temperature, radiation, loads and/or corrosive environments. This book analyzes the requirements on components, and introduces reference solutions regarding materials and processes. It describes the materials’ main properties, their limits and the current R&D trends. Lastly, innovations are discussed, such as materials with enhanced properties, advanced manufacturing or using AI.