Bayesian Optimization For Materials Science

Author : Daniel Packwood
Publisher : Springer
Page : 42 pages
File Size : 49,7 Mb
Release : 2017-10-04
Category : Technology & Engineering
ISBN : 9789811067815

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Bayesian Optimization for Materials Science by Daniel Packwood Pdf

This book provides a short and concise introduction to Bayesian optimization specifically for experimental and computational materials scientists. After explaining the basic idea behind Bayesian optimization and some applications to materials science in Chapter 1, the mathematical theory of Bayesian optimization is outlined in Chapter 2. Finally, Chapter 3 discusses an application of Bayesian optimization to a complicated structure optimization problem in computational surface science.Bayesian optimization is a promising global optimization technique that originates in the field of machine learning and is starting to gain attention in materials science. For the purpose of materials design, Bayesian optimization can be used to predict new materials with novel properties without extensive screening of candidate materials. For the purpose of computational materials science, Bayesian optimization can be incorporated into first-principles calculations to perform efficient, global structure optimizations. While research in these directions has been reported in high-profile journals, until now there has been no textbook aimed specifically at materials scientists who wish to incorporate Bayesian optimization into their own research. This book will be accessible to researchers and students in materials science who have a basic background in calculus and linear algebra.

Author : Turab Lookman,Francis J. Alexander,Krishna Rajan
Publisher : Springer
Page : 307 pages
File Size : 54,9 Mb
Release : 2015-12-12
Category : Technology & Engineering
ISBN : 9783319238715

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Information Science for Materials Discovery and Design by Turab Lookman,Francis J. Alexander,Krishna Rajan Pdf

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.

Author : Norbert Huber,Surya R. Kalidindi,Benjamin Klusemann,Christian Johannes Cyron
Publisher : Frontiers Media SA
Page : 235 pages
File Size : 54,7 Mb
Release : 2020-04-22
Category : Electronic
ISBN : 9782889636518

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Machine Learning and Data Mining in Materials Science by Norbert Huber,Surya R. Kalidindi,Benjamin Klusemann,Christian Johannes Cyron Pdf

Author : Keith T. Butler,Felipe Oviedo,Pieremanuele Canepa
Publisher : American Chemical Society
Page : 176 pages
File Size : 45,7 Mb
Release : 2022-06-16
Category : Technology & Engineering
ISBN : 9780841299467

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Machine Learning in Materials Science by Keith T. Butler,Felipe Oviedo,Pieremanuele Canepa Pdf

Machine Learning for Materials Science provides the fundamentals and useful insight into where Machine Learning (ML) will have the greatest impact for the materials science researcher. This digital primer provides example methods for ML applied to experiments and simulations, including the early stages of building an ML solution for a materials science problem, concentrating on where and how to get data and some of the considerations when choosing an approach. The authors demonstrate how to build more robust models, how to make sure that your colleagues trust the results, and how to use ML to accelerate or augment simulations, by introducing methods in which ML can be applied to analyze and process experimental data. They also cover how to build integrated closed-loop experiments where ML is used to plan the course of a materials optimization experiment and how ML can be utilized in the discovery of materials on computers.

Author : Francesco Archetti,Antonio Candelieri
Publisher : Springer Nature
Page : 126 pages
File Size : 55,5 Mb
Release : 2019-09-25
Category : Business & Economics
ISBN : 9783030244941

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Bayesian Optimization and Data Science by Francesco Archetti,Antonio Candelieri Pdf

This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems. The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities.

Author : Roman Garnett
Publisher : Cambridge University Press
Page : 376 pages
File Size : 52,9 Mb
Release : 2023-01-31
Category : Computers
ISBN : 9781108623551

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Bayesian Optimization by Roman Garnett Pdf

Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond. This timely text provides a self-contained and comprehensive introduction to the subject, starting from scratch and carefully developing all the key ideas along the way. This bottom-up approach illuminates unifying themes in the design of Bayesian optimization algorithms and builds a solid theoretical foundation for approaching novel situations. The core of the book is divided into three main parts, covering theoretical and practical aspects of Gaussian process modeling, the Bayesian approach to sequential decision making, and the realization and computation of practical and effective optimization policies. Following this foundational material, the book provides an overview of theoretical convergence results, a survey of notable extensions, a comprehensive history of Bayesian optimization, and an extensive annotated bibliography of applications.

Author : Isao Tanaka
Publisher : Springer
Page : 298 pages
File Size : 49,8 Mb
Release : 2018-01-15
Category : Technology & Engineering
ISBN : 9789811076176

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Nanoinformatics by Isao Tanaka Pdf

This open access book brings out the state of the art on how informatics-based tools are used and expected to be used in nanomaterials research. There has been great progress in the area in which “big-data” generated by experiments or computations are fully utilized to accelerate discovery of new materials, key factors, and design rules. Data-intensive approaches play indispensable roles in advanced materials characterization. "Materials informatics" is the central paradigm in the new trend. "Nanoinformatics" is its essential subset, which focuses on nanostructures of materials such as surfaces, interfaces, dopants, and point defects, playing a critical role in determining materials properties. There have been significant advances in experimental and computational techniques to characterize individual atoms in nanostructures and to gain quantitative information. The collaboration of researchers in materials science and information science is growing actively and is creating a new trend in materials science and engineering.

Author : Kristof T. Schütt,Stefan Chmiela,O. Anatole von Lilienfeld,Alexandre Tkatchenko,Koji Tsuda,Klaus-Robert Müller
Publisher : Springer Nature
Page : 473 pages
File Size : 52,7 Mb
Release : 2020-06-03
Category : Science
ISBN : 9783030402457

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Machine Learning Meets Quantum Physics by Kristof T. Schütt,Stefan Chmiela,O. Anatole von Lilienfeld,Alexandre Tkatchenko,Koji Tsuda,Klaus-Robert Müller Pdf

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.

Author : Dan Ohtan Wang,Daniel Packwood
Publisher : Springer Nature
Page : 255 pages
File Size : 50,5 Mb
Release : 2021-04-15
Category : Science
ISBN : 9783030559243

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Cell-Inspired Materials and Engineering by Dan Ohtan Wang,Daniel Packwood Pdf

This book highlights cutting-edge studies in the development of cell-inspired biomaterials and synthetic materials that manipulate cell functions and provide the next generation with contemporary tools for treating complex human diseases. It explores the convergence of synthetic materials with cell and molecular biology and surveys how functional materials, when patterned with spatial and temporal precision, can be used effectively to maintain cell proliferation and phenotype in vitro, to trigger specific cell functions, and to redirect cell-fate decisions. Human stem cells are a frequently discussed subject in this book. This is an ideal book for students, cell biologists, researchers interested in interdisciplinary research, and biomedical engineers. This book also: Highlights successfully developed technologies in cell engineering that make possible new therapeutic development for previously untreatable conditions Covers topics including bio-inspired micro patterning, DNA origami technology, synthetic NOS inspired by compartmentalized signaling in cells, and light-induced depolarization of the cell membrane Illustrates in detail the use of stem cells and synthetic scaffolds to model ethically sensitive embryonic tissues and organs

Author : Turab Lookman,Stephan Eidenbenz,Frank Alexander,Cris Barnes
Publisher : Springer
Page : 256 pages
File Size : 40,9 Mb
Release : 2018-09-22
Category : Science
ISBN : 9783319994659

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Materials Discovery and Design by Turab Lookman,Stephan Eidenbenz,Frank Alexander,Cris Barnes Pdf

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.

Author : German Sastre,Frits Daeyaert
Publisher : John Wiley & Sons
Page : 468 pages
File Size : 40,6 Mb
Release : 2023-01-25
Category : Science
ISBN : 9781119819776

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AI-Guided Design and Property Prediction for Zeolites and Nanoporous Materials by German Sastre,Frits Daeyaert Pdf

AI-Guided Design and Property Prediction for Zeolites and Nanoporous Materials A cohesive and insightful compilation of resources explaining the latest discoveries and methods in the field of nanoporous materials In Artificial Intelligence for Zeolites and Nanoporous Materials: Design, Synthesis and Properties Prediction a team of distinguished researchers delivers a robust compilation of the latest knowledge and most recent developments in computational chemistry, synthetic chemistry, and artificial intelligence as it applies to zeolites, porous molecular materials, covalent organic frameworks and metal-organic frameworks. The book presents a common language that unifies these fields of research and advances the discovery of new nanoporous materials. The editors have included resources that describe strategies to synthesize new nanoporous materials, construct databases of materials, structure directing agents, and synthesis conditions, and explain computational methods to generate new materials. They also offer material that discusses AI and machine learning algorithms, as well as other, similar approaches to the field. Readers will also find a comprehensive approach to artificial intelligence applied to and written in the language of materials chemistry, guiding the reader through the fundamental questions on how far computer algorithms and numerical representations can drive our search of new nanoporous materials for specific applications. Designed for academic researchers and industry professionals with an interest in synthetic nanoporous materials chemistry, Artificial Intelligence for Zeolites and Nanoporous Materials: Design, Synthesis and Properties Prediction will also earn a place in the libraries of professionals working in large energy, chemical, and biochemical companies with responsibilities related to the design of new nanoporous materials.

Author : Yutaka Wakayama,Katsuhiko Ariga
Publisher : Springer Nature
Page : 334 pages
File Size : 45,8 Mb
Release : 2022-01-03
Category : Technology & Engineering
ISBN : 9784431569121

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System-Materials Nanoarchitectonics by Yutaka Wakayama,Katsuhiko Ariga Pdf

This book is the first publication to widely introduce the contributions of nanoarchitectonics to the development of functional materials and systems. The book opens up pathways to novel nanotechnology based on bottom-up techniques. In fields of nanotechnology, theoretical and practical limitations are expected in the bottom-up nanofabrication process. Instead, some supramolecular processes for nano- and microstructure formation including molecular recognition, self-assembly, and template synthesis have gained great attention as novel key technologies to break through expected limitations in current nanotechnology. This volume describes future images of nanotechnology and related materials and device science as well as practical applications for energy and biotechnology. Readers including specialists, non-specialists, graduate students, and undergraduate students can focus on the parts of the book that interest and concern them most. Target fields include materials chemistry, organic chemistry, physical chemistry, nanotechnology, and even biotechnology.

Author : The Minerals, Metals & Materials Society
Publisher : Springer Nature
Page : 1062 pages
File Size : 51,7 Mb
Release : 2021-02-23
Category : Technology & Engineering
ISBN : 9783030652616

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TMS 2021 150th Annual Meeting & Exhibition Supplemental Proceedings by The Minerals, Metals & Materials Society Pdf

This collection presents papers from the 150th Annual Meeting & Exhibition of The Minerals, Metals & Materials Society.

Author : Takashiro Akitsu
Publisher : Elsevier
Page : 280 pages
File Size : 49,5 Mb
Release : 2021-10-08
Category : Science
ISBN : 9780128232729

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Computational and Data-Driven Chemistry Using Artificial Intelligence by Takashiro Akitsu Pdf

Computational and Data-Driven Chemistry Using Artificial Intelligence: Volume 1: Fundamentals, Methods and Applications highlights fundamental knowledge and current developments in the field, giving readers insight into how these tools can be harnessed to enhance their own work. Offering the ability to process large or complex data-sets, compare molecular characteristics and behaviors, and help researchers design or identify new structures, Artificial Intelligence (AI) holds huge potential to revolutionize the future of chemistry. Volume 1 explores the fundamental knowledge and current methods being used to apply AI across a whole host of chemistry applications. Drawing on the knowledge of its expert team of global contributors, the book offers fascinating insight into this rapidly developing field and serves as a great resource for all those interested in exploring the opportunities afforded by the intersection of chemistry and AI in their own work. Part 1 provides foundational information on AI in chemistry, with an introduction to the field and guidance on database usage and statistical analysis to help support newcomers to the field. Part 2 then goes on to discuss approaches currently used to address problems in broad areas such as computational and theoretical chemistry; materials, synthetic and medicinal chemistry; crystallography, analytical chemistry, and spectroscopy. Finally, potential future trends in the field are discussed. Provides an accessible introduction to the current state and future possibilities for AI in chemistry Explores how computational chemistry methods and approaches can both enhance and be enhanced by AI Highlights the interdisciplinary and broad applicability of AI tools across a wide range of chemistry fields

Author : A. Passerini,T. Schiex
Publisher : IOS Press
Page : 172 pages
File Size : 55,7 Mb
Release : 2022-08-05
Category : Computers
ISBN : 9781643682952

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PAIS 2022 by A. Passerini,T. Schiex Pdf

Artificial Intelligence (AI) is a central topic in contemporary computer science; one which has enabled many groundbreaking developments that have significantly influenced our society. Not only has it proved to be of fundamental importance in areas such as medicine, biology, economics, philosophy, linguistics, psychology and engineering, but it has also had a significant impact in a number of fields, including e-commerce, tourism, e-government, national security, manufacturing and other economic sectors. This book contains the proceedings of PAIS 2022, the 11th Conference on Prestigious Applications of Artificial Intelligence, held in Vienna, Austria, on 25 July 2022 as a satellite event of IJCAI-ECAI 2022. The PAIS conference invites papers describing innovative applications of AI techniques to real-world systems and problems, and aims to provide a forum for academic and industrial researchers and practitioners to share their experience and insight on the applicability, development and deployment of intelligent systems. A total of 18 full-paper submissions and 4 extended-abstract submissions were received for the 2022 conference, of which 10 full papers and 3 extended abstracts were accepted after rigorous peer review. The topics covered range from autonomous navigation, air traffic control and satellite management to the optimization of industrial processes and human-in-the-loop applications. The book will be of interest to all those whose work involves the innovative application of AI techniques to real-world situations.