Computational And Data Driven Chemistry Using Artificial Intelligence
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| 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
| 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 | : 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 | : Sidney Yip |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 2903 |
| Release | : 2007-11-17 |
| Genre | : Science |
| ISBN | : 1402032862 |
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 discussion of methods but also in chapters on crystal defects, microstructure, fluids, polymers and soft matter. Written by authors who are actively participating in the current development, this collection of 150 articles has the breadth and depth to be a major contributor toward defining the field of computational materials. In addition, there are 40 commentaries by highly respected researchers, presenting various views that should interest the future generations of the community. Subject Editors: Martin Bazant, MIT; Bruce Boghosian, Tufts University; Richard Catlow, Royal Institution; Long-Qing Chen, Pennsylvania State University; William Curtin, Brown University; Tomas Diaz de la Rubia, Lawrence Livermore National Laboratory; Nicolas Hadjiconstantinou, MIT; Mark F. Horstemeyer, Mississippi State University; Efthimios Kaxiras, Harvard University; L. Mahadevan, Harvard University; Dimitrios Maroudas, University of Massachusetts; Nicola Marzari, MIT; Horia Metiu, University of California Santa Barbara; Gregory C. Rutledge, MIT; David J. Srolovitz, Princeton University; Bernhardt L. Trout, MIT; Dieter Wolf, Argonne National Laboratory.
| 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.
| Author | : Paola Lecca |
| Publisher | : John Wiley & Sons |
| Total Pages | : 437 |
| Release | : 2024-07-29 |
| Genre | : Medical |
| ISBN | : 1119846536 |
Big Data Analysis and Artificial Intelligence for Medical Sciences Overview of the current state of the art on the use of artificial intelligence in medicine and biology Big Data Analysis and Artificial Intelligence for Medical Sciences demonstrates the efforts made in the fields of Computational Biology and medical sciences to design and implement robust, accurate, and efficient computer algorithms for modeling the behavior of complex biological systems much faster than using traditional modeling approaches based solely on theory. With chapters written by international experts in the field of medical and biological research, Big Data Analysis and Artificial Intelligence for Medical Sciences includes information on: Studies conducted by the authors which are the result of years of interdisciplinary collaborations with clinicians, computer scientists, mathematicians, and engineers Differences between traditional computational approaches to data processing (those of mathematical biology) versus the experiment-data-theory-model-validation cycle Existing approaches to the use of big data in the healthcare industry, such as through IBM’s Watson Oncology, Microsoft’s Hanover, and Google’s DeepMind Difficulties in the field that have arisen as a result of technological changes, and potential future directions these changes may take A timely and up-to-date resource on the integration of artificial intelligence in medicine and biology, Big Data Analysis and Artificial Intelligence for Medical Sciences is of great benefit not only to professional scholars, but also MSc or PhD program students eager to explore advancement in the field.
| 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 | : Sergio Rampino |
| Publisher | : Elsevier |
| Total Pages | : 296 |
| Release | : 2022-05-16 |
| Genre | : Science |
| ISBN | : 0323908667 |
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, chemical bonding, and cover theory to practice on three related physical problems (wavepacket dynamics, Hartree-Fock equations and electron-cloud redistribution). Drawing on the interdisciplinary knowledge of its expert author, this book provides a contemporary guide to theoretical and computational chemistry for all those working in chemical physics, physical chemistry and related fields. - Combines a 'big picture' overview of chemistry as it relates to physics and computer science, including detailed guidance on tackling chemistry problems from both theoretical and computational perspectives - Treats nuclear dynamics and electronic structure on the same footing in discussions of the Born-Oppenheimer approximation - Includes examples of scientific programming in modern Fortran for problems related to the modeling of chemical reaction dynamics and the analysis of chemical bonding
| Author | : Abby L. Parrill |
| Publisher | : John Wiley & Sons |
| Total Pages | : 486 |
| Release | : 2016-04-11 |
| Genre | : Science |
| ISBN | : 1119103932 |
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 Efficient Transition-State Modeling using Molecular Mechanics Force Fields for the Everyday Chemist Machine Learning in Materials Science: Recent Progress and Emerging Applications Discovering New Materials via a priori Crystal Structure Prediction Introduction to Maximally Localized Wannier Functions Methods for a Rapid and Automated Description of Proteins: Protein Structure, Protein Similarity, and Protein Folding
