Applications of Artificial Intelligence for Organic Chemistry
| Author | : Robert K. Lindsay |
| Publisher | : McGraw-Hill Companies |
| Total Pages | : 216 |
| Release | : 1980 |
| Genre | : Computers |
| ISBN | : |
Applications Of Artificial Intelligence For Organic Chemistry
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Artificial Intelligence in Chemistry
| Author | : José S. Torrecilla |
| Publisher | : Frontiers Media SA |
| Total Pages | : 89 |
| Release | : 2020-07-17 |
| Genre | : |
| ISBN | : 2889638707 |
Applications of Artificial Intelligence in Process Systems Engineering
| Author | : Jingzheng Ren |
| Publisher | : Elsevier |
| Total Pages | : 542 |
| Release | : 2021-06-05 |
| Genre | : Technology & Engineering |
| ISBN | : 012821743X |
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
Computational and Data-Driven Chemistry Using Artificial Intelligence
| Author | : Takashiro Akitsu |
| Publisher | : Elsevier |
| Total Pages | : 280 |
| Release | : 2021-10-08 |
| Genre | : Science |
| ISBN | : 0128232722 |
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
| Author | : Jon Paul Janet |
| Publisher | : American Chemical Society |
| Total Pages | : 189 |
| Release | : 2020-05-28 |
| Genre | : Science |
| ISBN | : 0841299005 |
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 chemistsderivations where they are important
Artificial Intelligence in Drug Discovery
| Author | : Nathan Brown |
| Publisher | : Royal Society of Chemistry |
| Total Pages | : 425 |
| Release | : 2020-11-04 |
| Genre | : Computers |
| ISBN | : 1839160543 |
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.
Machine Learning in Chemistry
| Author | : Edward O. Pyzer-Knapp |
| Publisher | : |
| Total Pages | : 140 |
| Release | : 2020-10-22 |
| Genre | : Science |
| ISBN | : 9780841235052 |
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
| Author | : Hugh M. Cartwright |
| Publisher | : Royal Society of Chemistry |
| Total Pages | : 564 |
| Release | : 2020-07-15 |
| Genre | : Science |
| ISBN | : 1788017897 |
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.
Artificial Intelligence in Chemistry
| Author | : Z. Hippe |
| Publisher | : Elsevier |
| Total Pages | : 292 |
| Release | : 2013-10-22 |
| Genre | : Computers |
| ISBN | : 1483291677 |
This comprehensive overview of the application of artificial intelligence methods (AI) in chemistry contains an in-depth summary of the most interesting achievements of modern AI, namely, problem-solving in molecular structure elucidation and in syntheses design. The book provides a brief history of AI as a branch of computer science. It also gives an overview of the basic methods employed for searching the solution space (thoroughly exemplified by chemical problems), together with a profound and expert discussion on many questions that may be raised by modern chemists wishing to apply computer-assisted methods in their own research. Moreover, it includes a survey of the most important literature references, covering all essential research in automated interpretation of molecular spectra to elucidate a structure and in syntheses design. A glossary of basic terms from computer technology for chemists is appended. This book is intended to make the emerging field of artificial intelligence understandable and accessible for chemists, who are not trained in computer methods for solving chemical problems. The author discusses step-by-step basic algorithms for structure elucidation and many aspects of the automated design of organic syntheses in order to integrate this fascinating technology into current chemical knowledge.
Fundamentals and Applications of Organic Electrochemistry
| Author | : Toshio Fuchigami |
| Publisher | : John Wiley & Sons |
| Total Pages | : 238 |
| Release | : 2014-11-10 |
| Genre | : Science |
| ISBN | : 1118653173 |
This textbook is an accessible overview of the broad field of organic electrochemistry, covering the fundamentals and applications of contemporary organic electrochemistry. The book begins with an introduction to the fundamental aspects of electrode electron transfer and methods for the electrochemical measurement of organic molecules. It then goes on to discuss organic electrosynthesis of molecules and macromolecules, including detailed experimental information for the electrochemical synthesis of organic compounds and conducting polymers. Later chapters highlight new methodology for organic electrochemical synthesis, for example electrolysis in ionic liquids, the application to organic electronic devices such as solar cells and LEDs, and examples of commercialized organic electrode processes. Appendices present useful supplementary information including experimental examples of organic electrosynthesis, and tables of physical data (redox potentials of various organic solvents and organic compounds and physical properties of various organic solvents).
