Computational and Data Driven Chemistry Using Artificial Intelligence Book

Computational and Data Driven Chemistry Using Artificial Intelligence


  • Author : Takashiro Akitsu
  • Publisher : Elsevier
  • Release Date : 2021-10-08
  • Genre: Science
  • Pages : 278
  • ISBN 10 : 9780128232729
  • Total Read : 86
  • File Size : 9,7 Mb

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Computational and Data Driven Chemistry Using Artificial Intelligence Summary:

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

Computational and Data Driven Chemistry Using Artificial Intelligence Book

Computational and Data Driven Chemistry Using Artificial Intelligence


  • Author : Takashiro Akitsu
  • Publisher : Elsevier
  • Release Date : 2021-10-12
  • Genre: Science
  • Pages : 278
  • ISBN 10 : 9780128222492
  • Total Read : 99
  • File Size : 11,5 Mb

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Computational and Data Driven Chemistry Using Artificial Intelligence Summary:

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

Machine Learning in Chemistry Book

Machine Learning in Chemistry


  • Author : Edward O. Pyzer-Knapp
  • Publisher : Unknown
  • Release Date : 2020-10-22
  • Genre: Science
  • Pages : 140
  • ISBN 10 : 0841235058
  • Total Read : 84
  • File Size : 20,9 Mb

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Machine Learning in Chemistry Summary:

Atomic-scale representation and statistical learning of tensorial properties -- Prediction of Mohs hardness with machine learning methods using compositional features -- High-dimensional neural network potentials for atomistic simulations -- Data-driven learning systems for chemical reaction prediction: an analysis of recent approaches -- Using machine learning to inform decisions in drug discovery : an industry perspective -- Cognitive materials discovery and onset of the 5th discovery paradigm.

Applications of Computational Intelligence in Data Driven Trading Book

Applications of Computational Intelligence in Data Driven Trading


  • Author : Cris Doloc
  • Publisher : John Wiley & Sons
  • Release Date : 2019-10-29
  • Genre: Business & Economics
  • Pages : 304
  • ISBN 10 : 9781119550501
  • Total Read : 81
  • File Size : 18,9 Mb

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Applications of Computational Intelligence in Data Driven Trading Summary:

“Life on earth is filled with many mysteries, but perhaps the most challenging of these is the nature of Intelligence.” – Prof. Terrence J. Sejnowski, Computational Neurobiologist The main objective of this book is to create awareness about both the promises and the formidable challenges that the era of Data-Driven Decision-Making and Machine Learning are confronted with, and especially about how these new developments may influence the future of the financial industry. The subject of Financial Machine Learning has attracted a lot of interest recently, specifically because it represents one of the most challenging problem spaces for the applicability of Machine Learning. The author has used a novel approach to introduce the reader to this topic: The first half of the book is a readable and coherent introduction to two modern topics that are not generally considered together: the data-driven paradigm and Computational Intelligence. The second half of the book illustrates a set of Case Studies that are contemporarily relevant to quantitative trading practitioners who are dealing with problems such as trade execution optimization, price dynamics forecast, portfolio management, market making, derivatives valuation, risk, and compliance. The main purpose of this book is pedagogical in nature, and it is specifically aimed at defining an adequate level of engineering and scientific clarity when it comes to the usage of the term “Artificial Intelligence,” especially as it relates to the financial industry. The message conveyed by this book is one of confidence in the possibilities offered by this new era of Data-Intensive Computation. This message is not grounded on the current hype surrounding the latest technologies, but on a deep analysis of their effectiveness and also on the author’s two decades of professional experience as a technologist, quant and academic.

Machine Learning in Chemistry Book

Machine Learning in Chemistry


  • Author : Jon Paul Janet
  • Publisher : American Chemical Society
  • Release Date : 2020-05-28
  • Genre: Science
  • Pages : null
  • ISBN 10 : 9780841299009
  • Total Read : 99
  • File Size : 14,5 Mb

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Machine Learning in Chemistry Summary:

Recent advances in machine learning or artificial intelligence for vision and natural language processing that have enabled the development of new technologies such as personal assistants or self-driving cars have brought machine learning and artificial intelligence to the forefront of popular culture. The accumulation of these algorithmic advances along with the increasing availability of large data sets and readily available high performance computing has played an important role in bringing machine learning applications to such a wide range of disciplines. Given the emphasis in the chemical sciences on the relationship between structure and function, whether in biochemistry or in materials chemistry, adoption of machine learning by chemists. Machine Learning in Chemistry focuses on the following to launch your understanding of this highly relevant topic: Topics most relevant to chemical sciences are the focus. Focus on concepts rather than technical details. Comprehensive referencing provides sources to go to for more technical details. Key details about methods that underlie machine learning (not easy, but important to understand the strengths as well as the limitations of these methods and to identify where domain knowledge can be most readily applied. Familiarity with basic single variable calculus and in linear algebra will be helpful although we have provided step-by-step derivations where they are important

Chemistry at the Frontier with Physics and Computer Science Book
Score: 5
From 1 Ratings

Chemistry at the Frontier with Physics and Computer Science


  • Author : Sergio Rampino
  • Publisher : Elsevier
  • Release Date : 2022-05-16
  • Genre: Science
  • Pages : 294
  • ISBN 10 : 9780323908665
  • Total Read : 74
  • File Size : 14,7 Mb

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Chemistry at the Frontier with Physics and Computer Science Summary:

Chemistry at the Frontier with Physics and Computer Science: Theory and Computation shows how chemical concepts relate to their physical counterparts and can be effectively explored via computational tools. It provides a holistic overview of the intersection of these fields and offers practical examples on how to solve a chemical problem from a theoretical and computational perspective, going from theory to models, methods and implementation. Sections cover both sides of the Born-Oppenheimer approximation (nuclear dynamics and electronic structure), chemical reactions, chemical bonding, and cover theory to practice on three related physical problems (wavepacket dynamics, Hartree-Fock equations and electron-cloud redistribution). Drawing on the interdisciplinary knowledge of its expert author, this book provides a contemporary guide to theoretical and computational chemistry for all those working in chemical physics, physical chemistry and related fields. Combines a ‘big picture’ overview of chemistry as it relates to physics and computer science, including detailed guidance on tackling chemistry problems from both theoretical and computational perspectives Treats nuclear dynamics and electronic structure on the same footing in discussions of the Born-Oppenheimer approximation Includes examples of scientific programming in modern Fortran for problems related to the modeling of chemical reaction dynamics and the analysis of chemical bonding

Handbook of Materials Modeling Book

Handbook of Materials Modeling


  • Author : Sidney Yip
  • Publisher : Springer Science & Business Media
  • Release Date : 2007-11-17
  • Genre: Science
  • Pages : 2965
  • ISBN 10 : 9781402032868
  • Total Read : 63
  • File Size : 8,9 Mb

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Handbook of Materials Modeling Summary:

The first reference of its kind in the rapidly emerging field of computational approachs to materials research, this is a compendium of perspective-providing and topical articles written to inform students and non-specialists of the current status and capabilities of modelling and simulation. From the standpoint of methodology, the development follows a multiscale approach with emphasis on electronic-structure, atomistic, and mesoscale methods, as well as mathematical analysis and rate processes. Basic models are treated across traditional disciplines, not only in the discussion of methods but also in chapters on crystal defects, microstructure, fluids, polymers and soft matter. Written by authors who are actively participating in the current development, this collection of 150 articles has the breadth and depth to be a major contributor toward defining the field of computational materials. In addition, there are 40 commentaries by highly respected researchers, presenting various views that should interest the future generations of the community. Subject Editors: Martin Bazant, MIT; Bruce Boghosian, Tufts University; Richard Catlow, Royal Institution; Long-Qing Chen, Pennsylvania State University; William Curtin, Brown University; Tomas Diaz de la Rubia, Lawrence Livermore National Laboratory; Nicolas Hadjiconstantinou, MIT; Mark F. Horstemeyer, Mississippi State University; Efthimios Kaxiras, Harvard University; L. Mahadevan, Harvard University; Dimitrios Maroudas, University of Massachusetts; Nicola Marzari, MIT; Horia Metiu, University of California Santa Barbara; Gregory C. Rutledge, MIT; David J. Srolovitz, Princeton University; Bernhardt L. Trout, MIT; Dieter Wolf, Argonne National Laboratory.

Machine Learning in Chemistry Book

Machine Learning in Chemistry


  • Author : Hugh M Cartwright
  • Publisher : Royal Society of Chemistry
  • Release Date : 2020-07-15
  • Genre: Science
  • Pages : 546
  • ISBN 10 : 9781839160240
  • Total Read : 67
  • File Size : 9,8 Mb

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Machine Learning in Chemistry Summary:

Progress in the application of machine learning (ML) to the physical and life sciences has been rapid. A decade ago, the method was mainly of interest to those in computer science departments, but more recently ML tools have been developed that show significant potential across wide areas of science. There is a growing consensus that ML software, and related areas of artificial intelligence, may, in due course, become as fundamental to scientific research as computers themselves. Yet a perception remains that ML is obscure or esoteric, that only computer scientists can really understand it, and that few meaningful applications in scientific research exist. This book challenges that view. With contributions from leading research groups, it presents in-depth examples to illustrate how ML can be applied to real chemical problems. Through these examples, the reader can both gain a feel for what ML can and cannot (so far) achieve, and also identify characteristics that might make a problem in physical science amenable to a ML approach. This text is a valuable resource for scientists who are intrigued by the power of machine learning and want to learn more about how it can be applied in their own field.