Computational and Data Driven Chemistry Using Artificial Intelligence

Computational and Data Driven Chemistry Using Artificial Intelligence
Author: Takashiro Akitsu
Publsiher: Elsevier
Total Pages: 278
Release: 2021-10-08
ISBN 10: 0128232722
ISBN 13: 9780128232729
Language: EN, FR, DE, ES & NL

Computational and Data Driven Chemistry Using Artificial Intelligence Book Review:

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

Machine Learning in Chemistry
Author: Edward O. Pyzer-Knapp,Teodoro Laino
Publsiher: Unknown
Total Pages: 140
Release: 2020-10-22
ISBN 10: 9780841235052
ISBN 13: 0841235058
Language: EN, FR, DE, ES & NL

Machine Learning in Chemistry Book Review:

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.

Data Science in Chemistry

Data Science in Chemistry
Author: Thorsten Gressling
Publsiher: Walter de Gruyter GmbH & Co KG
Total Pages: 540
Release: 2020-11-23
ISBN 10: 3110629453
ISBN 13: 9783110629453
Language: EN, FR, DE, ES & NL

Data Science in Chemistry Book Review:

The ever-growing wealth of information has led to the emergence of a fourth paradigm of science. This new field of activity – data science – includes computer science, mathematics and a given specialist domain. This book focuses on chemistry, explaining how to use data science for deep insights and take chemical research and engineering to the next level. It covers modern aspects like Big Data, Artificial Intelligence and Quantum computing.

Applications of Artificial Intelligence in Process Systems Engineering

Applications of Artificial Intelligence in Process Systems Engineering
Author: Jingzheng Ren,Weifeng Shen,Yi Man,Lichun DOng
Publsiher: Elsevier
Total Pages: 540
Release: 2021-06-05
ISBN 10: 012821743X
ISBN 13: 9780128217436
Language: EN, FR, DE, ES & NL

Applications of Artificial Intelligence in Process Systems Engineering Book Review:

Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis Gives direction to future development trends of AI technologies in chemical and process engineering

Handbook of Materials Modeling

Handbook of Materials Modeling
Author: Sidney Yip
Publsiher: Springer Science & Business Media
Total Pages: 2965
Release: 2007-11-17
ISBN 10: 1402032862
ISBN 13: 9781402032868
Language: EN, FR, DE, ES & NL

Handbook of Materials Modeling Book Review:

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

Machine Learning in Chemistry
Author: Hugh M Cartwright
Publsiher: Royal Society of Chemistry
Total Pages: 546
Release: 2020-07-15
ISBN 10: 1839160241
ISBN 13: 9781839160240
Language: EN, FR, DE, ES & NL

Machine Learning in Chemistry Book Review:

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.

Applications of Computational Intelligence in Data Driven Trading

Applications of Computational Intelligence in Data Driven Trading
Author: Cris Doloc
Publsiher: John Wiley & Sons
Total Pages: 304
Release: 2019-10-29
ISBN 10: 1119550505
ISBN 13: 9781119550501
Language: EN, FR, DE, ES & NL

Applications of Computational Intelligence in Data Driven Trading Book Review:

“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.

Reviews in Computational Chemistry

Reviews in Computational Chemistry
Author: Kenny B. Lipkowitz,Donald B. Boyd
Publsiher: John Wiley & Sons
Total Pages: 432
Release: 2003-05-08
ISBN 10: 0471458813
ISBN 13: 9780471458814
Language: EN, FR, DE, ES & NL

Reviews in Computational Chemistry Book Review:

Computational chemistry is increasingly used in most areas ofmolecular science including organic, inorganic, medicinal,biological, physical, and analytical chemistry. Researchers inthese fields who do molecular modelling need to understand and staycurrent with recent developments. This volume, like those prior toit, features chapters by experts in various fields of computationalchemistry. Two chapters focus on molecular docking, one of whichrelates to drug discovery and cheminformatics and the other toproteomics. In addition, this volume contains tutorials onspin-orbit coupling and cellular automata modeling, as well as anextensive bibliography of computational chemistry books. FROM REVIEWS OF THE SERIES "Reviews in Computational Chemistryremains the most valuable reference to methods and techniques incomputational chemistry."—JOURNAL OF MOLECULAR GRAPHICS ANDMODELLING "One cannot generally do better than to try to find anappropriate article in the highly successful Reviews inComputational Chemistry. The basic philosophy of the editors seemsto be to help the authors produce chapters that are complete,accurate, clear, and accessible to experimentalists (in particular)and other nonspecialists (in general)."—JOURNAL OF THEAMERICAN CHEMICAL SOCIETY

Artificial Intelligence in Drug Discovery

Artificial Intelligence in Drug Discovery
Author: Nathan Brown
Publsiher: Royal Society of Chemistry
Total Pages: 406
Release: 2020-11-11
ISBN 10: 1839160543
ISBN 13: 9781839160547
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Drug Discovery Book Review:

Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.

Data Science in Chemistry

Data Science in Chemistry
Author: Thorsten Gressling
Publsiher: Walter de Gruyter GmbH & Co KG
Total Pages: 540
Release: 2020-11-23
ISBN 10: 3110630532
ISBN 13: 9783110630534
Language: EN, FR, DE, ES & NL

Data Science in Chemistry Book Review:

The ever-growing wealth of information has led to the emergence of a fourth paradigm of science. This new field of activity – data science – includes computer science, mathematics and a given specialist domain. This book focuses on chemistry, explaining how to use data science for deep insights and take chemical research and engineering to the next level. It covers modern aspects like Big Data, Artificial Intelligence and Quantum computing.

Advances in Applications of Data Driven Computing

Advances in Applications of Data Driven Computing
Author: Jagdish Chand Bansal,Lance C. C. Fung,Milan Simic,Ankush Ghosh
Publsiher: Springer Nature
Total Pages: 182
Release: 2021-04-16
ISBN 10: 9813369191
ISBN 13: 9789813369191
Language: EN, FR, DE, ES & NL

Advances in Applications of Data Driven Computing Book Review:

This book aims to foster machine and deep learning approaches to data-driven applications, in which data governs the behaviour of applications. Applications of Artificial intelligence (AI)-based systems play a significant role in today’s software industry. The sensors data from hardware-based systems making a mammoth database, increasing day by day. Recent advances in big data generation and management have created an avenue for decision-makers to utilize these huge volumes of data for different purposes and analyses. AI-based application developers have long utilized conventional machine learning techniques to design better user interfaces and vulnerability predictions. However, with the advancement of deep learning-based and neural-based networks and algorithms, researchers are able to explore and learn more about data and their exposed relationships or hidden features. This new trend of developing data-driven application systems seeks the adaptation of computational neural network algorithms and techniques in many application domains, including software systems, cyber security, human activity recognition, and behavioural modelling. As such, computational neural networks algorithms can be refined to address problems in data-driven applications. Original research and review works with model and build data-driven applications using computational algorithm are included as chapters in this book.

Applications of Artificial Intelligence in COVID 19

Applications of Artificial Intelligence in COVID 19
Author: Sachi Nandan Mohanty
Publsiher: Springer Nature
Total Pages: 135
Release: 2022
ISBN 10: 9811573174
ISBN 13: 9789811573170
Language: EN, FR, DE, ES & NL

Applications of Artificial Intelligence in COVID 19 Book Review:

Artificial Intelligence in Chemistry

Artificial Intelligence in Chemistry
Author: José S. Torrecilla,John C. Cancilla,Jose Omar Valderrama,Charalampos Vasilios Proestos
Publsiher: Frontiers Media SA
Total Pages: 135
Release: 2020-07-17
ISBN 10: 2889638707
ISBN 13: 9782889638703
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Chemistry Book Review:

Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines

Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines
Author: Jihad Badra,Pinaki Pal,Yuanjiang Pei,Sibendu Som
Publsiher: Elsevier
Total Pages: 260
Release: 2022-01-21
ISBN 10: 032388458X
ISBN 13: 9780323884587
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines Book Review:

Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines summarizes recent developments in Artificial Intelligence (AI)/Machine Learning (ML) and data driven optimization and calibration techniques for internal combustion engines. The book covers AI/ML and data driven methods to optimize fuel formulations and engine combustion systems, predict cycle to cycle variations, and optimize after-treatment systems and experimental engine calibration. It contains all the details of the latest optimization techniques along with their application to ICE, making it ideal for automotive engineers, mechanical engineers, OEMs and R&D centers involved in engine design. Provides AI/ML and data driven optimization techniques in combination with Computational Fluid Dynamics (CFD) to optimize engine combustion systems Features a comprehensive overview of how AI/ML techniques are used in conjunction with simulations and experiments Discusses data driven optimization techniques for fuel formulations and vehicle control calibration

Emerging Trends in Data Driven Computing and Communications

Emerging Trends in Data Driven Computing and Communications
Author: Rajeev Mathur,C. P. Gupta,Vaibhav Katewa,Dharm Singh Jat,Neha Yadav
Publsiher: Springer Nature
Total Pages: 354
Release: 2021-09-27
ISBN 10: 9811639159
ISBN 13: 9789811639159
Language: EN, FR, DE, ES & NL

Emerging Trends in Data Driven Computing and Communications Book Review:

This book includes best selected, high-quality research papers presented at International Conference on Data Driven Computing and IoT (DDCIoT 2021) organized jointly by Geetanjali Institute of Technical Studies (GITS), Udaipur, and Rajasthan Technical University, Kota, India, during March 20–21, 2021. This book presents influential ideas and systems in the field of data driven computing, information technology, and intelligent systems.

Advances in Artificial Intelligence Computation and Data Science

Advances in Artificial Intelligence  Computation  and Data Science
Author: Tuan D. Pham,Hong Yan,Muhammad W. Ashraf,Folke Sjöberg
Publsiher: Springer Nature
Total Pages: 369
Release: 2021-07-12
ISBN 10: 303069951X
ISBN 13: 9783030699512
Language: EN, FR, DE, ES & NL

Advances in Artificial Intelligence Computation and Data Science Book Review:

Artificial intelligence (AI) has become pervasive in most areas of research and applications. While computation can significantly reduce mental efforts for complex problem solving, effective computer algorithms allow continuous improvement of AI tools to handle complexity—in both time and memory requirements—for machine learning in large datasets. Meanwhile, data science is an evolving scientific discipline that strives to overcome the hindrance of traditional skills that are too limited to enable scientific discovery when leveraging research outcomes. Solutions to many problems in medicine and life science, which cannot be answered by these conventional approaches, are urgently needed for society. This edited book attempts to report recent advances in the complementary domains of AI, computation, and data science with applications in medicine and life science. The benefits to the reader are manifold as researchers from similar or different fields can be aware of advanced developments and novel applications that can be useful for either immediate implementations or future scientific pursuit. Features: Considers recent advances in AI, computation, and data science for solving complex problems in medicine, physiology, biology, chemistry, and biochemistry Provides recent developments in three evolving key areas and their complementary combinations: AI, computation, and data science Reports on applications in medicine and physiology, including cancer, neuroscience, and digital pathology Examines applications in life science, including systems biology, biochemistry, and even food technology This unique book, representing research from a team of international contributors, has not only real utility in academia for those in the medical and life sciences communities, but also a much wider readership from industry, science, and other areas of technology and education.

Theoretical and Computational Chemistry

Theoretical and Computational Chemistry
Author: Iwona Gulaczyk,Bartosz Tylkowski
Publsiher: Walter de Gruyter GmbH & Co KG
Total Pages: 270
Release: 2021-06-08
ISBN 10: 3110678217
ISBN 13: 9783110678215
Language: EN, FR, DE, ES & NL

Theoretical and Computational Chemistry Book Review:

This book explores the applications of computational chemistry ranging from the pharmaceutical industry and molecular structure determination to spectroscopy and astrophysics. The authors detail how calculations can be used to solve a wide range of practical challenges encountered in research and industry.

Advances in Artificial Intelligence and Applied Cognitive Computing

Advances in Artificial Intelligence and Applied Cognitive Computing
Author: Hamid R. Arabnia
Publsiher: Springer Nature
Total Pages: 135
Release: 2022
ISBN 10: 3030702960
ISBN 13: 9783030702960
Language: EN, FR, DE, ES & NL

Advances in Artificial Intelligence and Applied Cognitive Computing Book Review:

Understanding Protein Dynamics Binding and Allostery for Drug Design

Understanding Protein Dynamics  Binding and Allostery for Drug Design
Author: Guang Hu,Pemra Doruker,Hongchun Li,Ebru Demet Akten
Publsiher: Frontiers Media SA
Total Pages: 135
Release: 2021-06-08
ISBN 10: 2889668487
ISBN 13: 9782889668489
Language: EN, FR, DE, ES & NL

Understanding Protein Dynamics Binding and Allostery for Drug Design Book Review:

Computational Toxicology

Computational Toxicology
Author: Sean Ekins
Publsiher: John Wiley & Sons
Total Pages: 800
Release: 2007-07-27
ISBN 10: 9780470145883
ISBN 13: 0470145889
Language: EN, FR, DE, ES & NL

Computational Toxicology Book Review:

A comprehensive analysis of state-of-the-art molecular modeling approaches and strategies applied to risk assessment for pharmaceutical and environmental chemicals This unique volume describes how the interaction of molecules with toxicologically relevant targets can be predicted using computer-based tools utilizing X-ray crystal structures or homology, receptor, pharmacophore, and quantitative structure activity relationship (QSAR) models of human proteins. It covers the in vitro models used, newer technologies, and regulatory aspects. The book offers a complete systems perspective to risk assessment prediction, discussing experimental and computational approaches in detail, with: * An introduction to toxicology methods and an explanation of computational methods * In-depth reviews of QSAR methods applied to enzymes, transporters, nuclear receptors, and ion channels * Sections on applying computers to toxicology assessment in the pharmaceutical industry and in the environmental arena * Chapters written by leading international experts * Figures that illustrate computational models and references for further information This is a key resource for toxicologists and scientists in the pharmaceutical industry and environmental sciences as well as researchers involved in ADMET, drug discovery, and technology and software development.