Computational and Data Driven Chemistry Using Artificial Intelligence

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  • Author : Takashiro Akitsu
  • Publisher : Elsevier
  • Pages : 278 pages
  • ISBN : 0128222492
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>Computational and Data Driven Chemistry Using Artificial Intelligence

Download or Read online Computational and Data Driven Chemistry Using Artificial Intelligence full in PDF, ePub and kindle. this book written by Takashiro Akitsu and published by Elsevier which was released on 29 October 2021 with total page 278 pages. We cannot guarantee that Computational and Data Driven Chemistry Using Artificial Intelligence book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. 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

Computational and Data Driven Chemistry Using Artificial Intelligence
  • Author : Takashiro Akitsu
  • Publisher : Elsevier
  • Release : 29 October 2021
GET THIS BOOK 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

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 BOOK 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

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 : 02 December 2021
GET THIS BOOK Advances 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-or 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

Data Science in Chemistry

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

Machine Learning in Chemistry

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

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 BOOK Emerging 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.

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 BOOK Theoretical 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.

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 : 05 June 2021
GET THIS BOOK Applications 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

Computational Modeling From Chemistry To Materials To Biology Proceedings Of The 25th Solvay Conference On Chemistry

Computational Modeling  From Chemistry To Materials To Biology   Proceedings Of The 25th Solvay Conference On Chemistry
  • Author : Kurt Wuthrich,Bert Weckhuysen,Laurence Rongy,Anne De Wit
  • Publisher : World Scientific
  • Release : 21 December 2020
GET THIS BOOK Computational Modeling From Chemistry To Materials To Biology Proceedings Of The 25th Solvay Conference On Chemistry

Chaired by K Wüthrich (Nobel Laureate in Chemistry, 2002) and co-chaired by B Weckhuysen, this by-invitation-only conference has gathered 39 participants — who are leaders in the field of computational modeling and its applications in Chemistry, Material Sciences and Biology. Highlights of the Conference Proceedings are short, prepared statements by all the participants and the records of lively discussions on the current and future perspectives in the field of computational modeling, from chemistry to materials to biology.

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 BOOK Reviews 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

Artificial Intelligence for Materials Science

Artificial Intelligence for Materials Science
  • Author : Yuan Cheng,Tian Wang,Gang Zhang
  • Publisher : Springer Nature
  • Release : 02 December 2021
GET THIS BOOK Artificial Intelligence for Materials Science

Machine learning methods have lowered the cost of exploring new structures of unknown compounds, and can be used to predict reasonable expectations and subsequently validated by experimental results. As new insights and several elaborative tools have been developed for materials science and engineering in recent years, it is an appropriate time to present a book covering recent progress in this field. Searchable and interactive databases can promote research on emerging materials. Recently, databases containing a large number of high-quality materials

Artificial Intelligence in Drug Discovery

Artificial Intelligence in Drug Discovery
  • Author : Nathan Brown
  • Publisher : Royal Society of Chemistry
  • Release : 11 November 2020
GET THIS BOOK Artificial 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