Applications Of Machine Learning In Analytical Chemistry
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| 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
| Author | : Dr Manish Kumar Thimmaraju |
| Publisher | : Blue Rose Publishers |
| Total Pages | : 340 |
| Release | : 2023-03-22 |
| Genre | : Education |
| ISBN | : |
“Applications of Machine Learning in Analytical Chemistry" is a comprehensive guide for anyone interested in understanding the fundamentals of ML and its application in analytical chemistry. The book is divided into 5 units, starting with ML basics such as categories, tools, data cleaning, and setup. The 2nd unit covers various ML algorithms such as regression, classification, clustering, ensemble modeling, and deep learning. The 3rd unit is dedicated to ML techniques in chemical product engineering, covering solutions for chemical product engineering issues, and chemical reaction forecasting. The 4th unit focuses on big data and ML for chemistry, discussing compound identification, ML-based synthesis prediction, and electronic drug design. The 5th and final unit discusses biosensors and sensors for the internet of things and intelligent systems, highlighting their potential applications and present limitations. The book is an excellent resource for analytical chemists, data scientists, and anyone interested in exploring the applications of ML in analytical 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.
| 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.
| Author | : Pavlo O. Dral |
| Publisher | : Elsevier |
| Total Pages | : 702 |
| Release | : 2022-09-16 |
| Genre | : Science |
| ISBN | : 0323886043 |
Quantum chemistry is simulating atomistic systems according to the laws of quantum mechanics, and such simulations are essential for our understanding of the world and for technological progress. Machine learning revolutionizes quantum chemistry by increasing simulation speed and accuracy and obtaining new insights. However, for nonspecialists, learning about this vast field is a formidable challenge. Quantum Chemistry in the Age of Machine Learning covers this exciting field in detail, ranging from basic concepts to comprehensive methodological details to providing detailed codes and hands-on tutorials. Such an approach helps readers get a quick overview of existing techniques and provides an opportunity to learn the intricacies and inner workings of state-of-the-art methods. The book describes the underlying concepts of machine learning and quantum chemistry, machine learning potentials and learning of other quantum chemical properties, machine learning-improved quantum chemical methods, analysis of Big Data from simulations, and materials design with machine learning. Drawing on the expertise of a team of specialist contributors, this book serves as a valuable guide for both aspiring beginners and specialists in this exciting field. - Compiles advances of machine learning in quantum chemistry across different areas into a single resource - Provides insights into the underlying concepts of machine learning techniques that are relevant to quantum chemistry - Describes, in detail, the current state-of-the-art machine learning-based methods in quantum chemistry
| Author | : Silvio Vaz Jr |
| Publisher | : Springer Nature |
| Total Pages | : 236 |
| Release | : 2023-08-31 |
| Genre | : Science |
| ISBN | : 3031389522 |
This book deals with analytical techniques and methods applied in several sectors of technology and industry and serves as a concise and up-to-date reference for the practical application of analytical chemistry. Divided into 10 chapters, the book starts with an introduction to the fundamentals of analytical chemistry, followed by a review of modern analytical technologies and their application in different industrial sectors and activities such as agrochemicals and pharmaceuticals, ores and mining, polymers, biotechnology, and oil & gas. Particular attention is given to industrial environmental issues, where the author discusses the advanced analytical techniques used to provide quantitative information about pollutants in aqueous and gaseous effluents and their carbon footprint. The book finishes with a chapter summarizing the main remarks and conclusions on advanced analytical techniques used in different industrial sectors, as well as on topics of sustainability, and instrumental analysis. In this book, readers will find valuable insights, including real-life examples, of how classical and instrumental techniques can be used by industry, to help professionals in the quality control of raw materials, products and processes, in the assessment of the formulation contamination, environmental pollution and in the evaluation of sustainability, among others. Given its breadth, the book appeals to professionals (mainly chemists, biochemists, and engineers), researchers, professors, and graduate students.
| Author | : Luigi Piroddi |
| Publisher | : Springer Nature |
| Total Pages | : 151 |
| Release | : 2022-01-01 |
| Genre | : Technology & Engineering |
| ISBN | : 3030859185 |
This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2020-21 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists.
| Author | : Mettu Srinivas |
| Publisher | : John Wiley & Sons |
| Total Pages | : 372 |
| Release | : 2021-08-10 |
| Genre | : Computers |
| ISBN | : 1119769248 |
Machine Learning Algorithms is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms. The book discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the user can easily move from the equations in the book to a computer program.
| Author | : Yuan Cheng |
| Publisher | : Springer Nature |
| Total Pages | : 231 |
| Release | : 2021-03-26 |
| Genre | : Technology & Engineering |
| ISBN | : 3030683109 |
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 properties for new advanced materials discovery have been developed. These approaches are set to make a significant impact on human life and, with numerous commercial developments emerging, will become a major academic topic in the coming years. This authoritative and comprehensive book will be of interest to both existing researchers in this field as well as others in the materials science community who wish to take advantage of these powerful techniques. The book offers a global spread of authors, from USA, Canada, UK, Japan, France, Russia, China and Singapore, who are all world recognized experts in their separate areas. With content relevant to both academic and commercial points of view, and offering an accessible overview of recent progress and potential future directions, the book will interest graduate students, postgraduate researchers, and consultants and industrial engineers.
| Author | : Ankur Choudhary |
| Publisher | : Springer Nature |
| Total Pages | : 738 |
| Release | : 2021-07-27 |
| Genre | : Computers |
| ISBN | : 9811630674 |
The book presents a collection of peer-reviewed articles from the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning - ICAAAIML 2020. The book covers research in artificial intelligence, machine learning, and deep learning applications in healthcare, agriculture, business, and security. This volume contains research papers from academicians, researchers as well as students. There are also papers on core concepts of computer networks, intelligent system design and deployment, real-time systems, wireless sensor networks, sensors and sensor nodes, software engineering, and image processing. This book will be a valuable resource for students, academics, and practitioners in the industry working on AI applications.
