Computational 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 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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  • Author : Takashiro Akitsu
  • Publisher : Elsevier
  • Pages : 278 pages
  • ISBN : 0128232722
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKComputational and Data Driven Chemistry Using Artificial Intelligence

Computational and Data-Driven Chemistry Using Artificial Intelligence

Computational and Data-Driven Chemistry Using Artificial Intelligence
  • Author : Takashiro Akitsu
  • Publisher : Elsevier
  • Release : 08 October 2021
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

Machine Learning in Chemistry

Machine Learning in Chemistry
  • Author : Edward O. Pyzer-Knapp,Teodoro Laino
  • Publisher : Unknown Publisher
  • Release : 22 October 2020
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.

Data Science in Chemistry

Data Science in Chemistry
  • Author : Thorsten Gressling
  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release : 23 November 2020
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.

The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry

The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry
  • Author : Stephanie K. Ashenden
  • Publisher : Academic Press
  • Release : 23 April 2021
GET THIS BOOKThe Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry

The Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry examines the drug discovery process, assessing how new technologies have improved effectiveness. Artificial intelligence and machine learning are considered the future for a wide range of disciplines and industries, including the pharmaceutical industry. In an environment where producing a single approved drug costs millions and takes many years of rigorous testing prior to its approval, reducing costs and time is of high interest. This book follows

Artificial Intelligence in Education

Artificial Intelligence in Education
  • Author : Matthew N.O. Sadiku,Sarhan M. Musa,Uwakwe C. Chukwu
  • Publisher : iUniverse
  • Release : 27 January 2022
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

Machine Learning in Chemistry

Machine Learning in Chemistry
  • Author : Hugh M Cartwright
  • Publisher : Royal Society of Chemistry
  • Release : 15 July 2020
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

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
  • Publisher : Springer Nature
  • Release : 12 July 2021
GET THIS BOOKAdvances in Artificial Intelligence, Computation, and Data Science

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

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 : 27 September 2021
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.

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 : 21 January 2022
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

Theoretical and Computational Chemistry

Theoretical and Computational Chemistry
  • Author : Iwona Gulaczyk,Bartosz Tylkowski
  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release : 08 June 2021
GET THIS BOOKTheoretical and Computational Chemistry

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.

Computational Sciences and Artificial Intelligence in Industry

Computational Sciences and Artificial Intelligence in Industry
  • Author : Tero Tuovinen,Jacques Periaux,Pekka Neittaanmäki
  • Publisher : Springer Nature
  • Release : 19 August 2021
GET THIS BOOKComputational Sciences and Artificial Intelligence in Industry

This book is addressed to young researchers and engineers in the fields of Computational Science and Artificial Intelligence, ranging from innovative computational methods to digital machine learning tools and their coupling used for solving challenging industrial and societal problems.This book provides the latest knowledge from jointly academic and industries experts in Computational Science and Artificial Intelligence fields for exploring possibilities and identifying challenges of applying Computational Sciences and AI methods and tools in industrial and societal sectors.

Artificial Intelligence for Data-Driven Medical Diagnosis

Artificial Intelligence for Data-Driven Medical Diagnosis
  • Author : Deepak Gupta,Utku Kose,Bao Le Nguyen,Siddhartha Bhattacharyya
  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release : 28 January 2021
GET THIS BOOKArtificial Intelligence for Data-Driven Medical Diagnosis

This book collects research works of data-driven medical diagnosis done via Artificial Intelligence based solutions, such as Machine Learning, Deep Learning and Intelligent Optimization. Physical devices powered with Artificial Intelligence are gaining importance in diagnosis and healthcare. Medical data from different sources can also be analyzed via Artificial Intelligence techniques for more effective results.

Reviews in Computational Chemistry

Reviews in Computational Chemistry
  • Author : Abby L. Parrill,Kenny B. Lipkowitz
  • Publisher : John Wiley & Sons
  • Release : 09 March 2016
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