Computational and Data Driven Chemistry Using Artificial Intelligence

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  • Publisher : Elsevier
  • Release : 08 October 2021
  • ISBN : 9780128232729
  • Page : 278 pages
  • Rating : 4.5/5 from 103 voters

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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

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Computational and Data-Driven Chemistry Using Artificial Intelligence

Computational and Data-Driven Chemistry Using Artificial Intelligence
  • Author : Takashiro Akitsu
  • Publisher : Elsevier
  • Release Date : 2021-10-08
  • ISBN : 9780128232729
GET THIS BOOKComputational and Data-Driven Chemistry Using Artificial Intelligence

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

Computational and Data-Driven Chemistry Using Artificial Intelligence

Computational and Data-Driven Chemistry Using Artificial Intelligence
  • Author : Takashiro Akitsu
  • Publisher : Elsevier
  • Release Date : 2021-10-29
  • ISBN : 9780128222492
GET THIS BOOKComputational and Data-Driven Chemistry Using Artificial Intelligence

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

Applications of Computational Intelligence in Data-Driven Trading

Applications of Computational Intelligence in Data-Driven Trading
  • Author : Cris Doloc
  • Publisher : John Wiley & Sons
  • Release Date : 2019-10-29
  • ISBN : 9781119550501
GET THIS BOOKApplications of Computational Intelligence in Data-Driven Trading

“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

Machine Learning in Chemistry

Machine Learning in Chemistry
  • Author : Edward O. Pyzer-Knapp,Teodoro Laino
  • Publisher : Unknown
  • Release Date : 2020-10-22
  • ISBN : 0841235058
GET THIS BOOKMachine Learning in Chemistry

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.

Machine Learning in Chemistry

Machine Learning in Chemistry
  • Author : Hugh M Cartwright
  • Publisher : Royal Society of Chemistry
  • Release Date : 2020-07-15
  • ISBN : 9781839160240
GET THIS BOOKMachine Learning in Chemistry

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

Chemistry at the Frontier with Physics and Computer Science

Chemistry at the Frontier with Physics and Computer Science
  • Author : Sergio Rampino
  • Publisher : Elsevier
  • Release Date : 2022-05-16
  • ISBN : 9780323908665
GET THIS BOOKChemistry at the Frontier with Physics and Computer Science

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,

Handbook of Materials Modeling

Handbook of Materials Modeling
  • Author : Sidney Yip
  • Publisher : Springer Science & Business Media
  • Release Date : 2007-11-17
  • ISBN : 9781402032868
GET THIS BOOKHandbook of Materials Modeling

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

Artificial Intelligence in Education

Artificial Intelligence in Education
  • Author : Matthew N.O. Sadiku,Sarhan M. Musa,Uwakwe C. Chukwu
  • Publisher : iUniverse
  • Release Date : 2022-01-27
  • ISBN : 9781663234346
GET THIS BOOKArtificial Intelligence in Education

The quest for building an artificial brain developed in the fields of computer science and psychology. Artificial intelligence (AI), sometimes called machine intelligence, refers to intelligence demonstrated by machines, while the natural intelligence is the intelligence displayed by humans and animals. Typically, AI systems demonstrate at least some of the following human behaviors: planning, learning, reasoning, problem solving, knowledge representation, perception, speech recognition, decision-making, language translation, motion, manipulation, intelligence, and creativity. Artificial intelligence is an emerging technology which the educational

Artificial Intelligence in Drug Discovery

Artificial Intelligence in Drug Discovery
  • Author : Nathan Brown
  • Publisher : Royal Society of Chemistry
  • Release Date : 2020-11-11
  • ISBN : 9781839160547
GET THIS BOOKArtificial Intelligence in Drug Discovery

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

Computational Toxicology

Computational Toxicology
  • Author : Sean Ekins
  • Publisher : John Wiley & Sons
  • Release Date : 2007-07-27
  • ISBN : 0470145889
GET THIS BOOKComputational Toxicology

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

Reviews in Computational Chemistry

Reviews in Computational Chemistry
  • Author : Abby L. Parrill,Kenny B. Lipkowitz
  • Publisher : John Wiley & Sons
  • Release Date : 2016-03-09
  • ISBN : 9781119157557
GET THIS BOOKReviews in Computational Chemistry

The Reviews in Computational Chemistry series brings together leading authorities in the field to teach the newcomer and update the expert on topics centered on molecular modeling, such as computer-assisted molecular design (CAMD), quantum chemistry, molecular mechanics and dynamics, and quantitative structure-activity relationships (QSAR). This volume, like those prior to it, features chapters by experts in various fields of computational chemistry. Topics in Volume 29 include: Noncovalent Interactions in Density-Functional Theory Long-Range Inter-Particle Interactions: Insights from Molecular Quantum Electrodynamics (QED) Theory

Data Science in Chemistry

Data Science in Chemistry
  • Author : Thorsten Gressling
  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release Date : 2020-11-23
  • ISBN : 9783110630534
GET THIS BOOKData Science in Chemistry

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.

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
  • Publisher : Springer Nature
  • Release Date : 2021-09-27
  • ISBN : 9789811639159
GET THIS BOOKEmerging Trends in Data Driven Computing and Communications

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.

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
  • Publisher : Elsevier
  • Release Date : 2021-06-05
  • ISBN : 9780128217436
GET THIS BOOKApplications of Artificial Intelligence in Process Systems Engineering

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

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
  • Publisher : Elsevier
  • Release Date : 2022-01-21
  • ISBN : 9780323884587
GET THIS BOOKArtificial Intelligence and Data Driven Optimization of Internal Combustion Engines

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