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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
Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
`Quantum Chemistry [the branch of Computational Chemistry that applies the laws of Quantum Mechanics to chemical systems] is one of the most dynamic fields of contemporary chemistry, providing a solid foundation for all of chemistry, and serving as the basis for practical, computational methodologies with applications in virtually all branches of chemistry ... The increased sophistication, accuracy and scope of the theory of chemistry are due to a large extent to the spectacular development of quantum chemistry, and in this book the authors have made a remarkable effort to provide a modern account of the field.' From the Foreword by Paul Mezey, University of Saskatchewan. Quantum Chemistry: Fundamentals to Applications develops quantum chemistry all the way from the fundamentals, found in Part I, through the applications that make up Part II. The applications include: molecular structure; spectroscopy; thermodynamics; chemical reactions; solvent effects; and excited state chemistry. The importance of this field is underscored by the fact that the 1998 Nobel Prize in Chemistry was awarded for the development of Quantum Chemistry.
The rivers run into the sea, yet the sea is not full Ecclesiastes What is quantum chemistry? The straightforward answer is that it is what quan tum chemists do. But it must be admitted, that in contrast to physicists and chemists, "quantum chemists" seem to be a rather ill-defined category of scientists. Quantum chemists are more or less physicists (basically theoreticians), more or less chemists, and by large, computationists. But first and foremost, we, quantum chemists, are conscious beings. We may safely guess that quantum chemistry was one of the first areas in the natural sciences to lie on the boundaries of many disciplines. We may certainly claim that quantum chemists were the first to use computers for really large scale calculations. The scope of the problems which quantum chemistry wishes to answer and which, by its unique nature, only quantum chemistry can only answer is growing daily. Retrospectively we may guess that many of those problems meet a daily need, or are say, technical in some sense. The rest are fundamental or conceptual. The daily life of most quantum chemists is usually filled with grasping the more or less technical problems. But it is at least as important to devote some time to the other kind of problems whose solution will open up new perspectives for both quantum chemistry itself and for the natural sciences in general.
"From Schrödinger's Equation to Deep Learning: A Quantum Approach" offers a captivating exploration that bridges the realms of quantum mechanics and deep learning. Tailored for scientists, researchers, and enthusiasts in both quantum physics and artificial intelligence, this book delves into the symbiotic relationship between quantum principles and cutting-edge deep learning techniques. Covering topics such as quantum-inspired algorithms, neural networks, and computational advancements, the book provides a comprehensive overview of how quantum approaches enrich and influence the field of deep learning. With clarity and depth, it serves as an enlightening resource for those intrigued by the dynamic synergy between quantum mechanics and the transformative potential of deep learning.
"Focusing on the journey from understanding Schrödinger's Equation to exploring the depths of Deep Learning, this book serves as a comprehensive guide for absolute beginners with no mathematical backgrounds. Starting with fundamental concepts in quantum mechanics, the book gradually introduces readers to the intricacies of Schrödinger's Equation and its applications in various fields. With clear explanations and accessible language, readers will delve into the principles of quantum mechanics and learn how they intersect with modern technologies such as Deep Learning. By bridging the gap between theoretical physics and practical applications, this book equips readers with the knowledge and skills to navigate the fascinating world of quantum mechanics and embark on the exciting journey of Deep Learning."
At the time when increasing numbers of chemists are being attracted by the fascination of supposedly easy computing and associated colourful imaging, this book appears as a counterpoint. The first part focuses on fundamental concepts of quantum chemistry, covering MCSCF theory, perturbation treatments, basis set developments, density matrices, wave function instabilities to correlation effects, and momentum space theory. The second part is devoted to more practical studies, ranging from the characterisation of exotic interstellar molecules, the accurate determination of spectroscopic constants, excited states structures and EPR parameters through photochemical and charge-transfer processes, cluster chemistry and fullerenes, muonium chemistry, to the possible prediction of the response of materials to electric fields in view of nonlinear optical applications. Audience: Graduate students and researchers whose work involves quantum chemistry, molecular physics, and materials modelling.
This volume, edited by a well-known specialist in the field of theoretical chemistry, gathers together a selection of papers on theoretical chemistry within the themes of mathematical, computational, and quantum chemistry. The authors present a rich assembly of some of the most important current research in the field of quantum chemistry in modern times. In Quantum Chemistry at the Dawn of the 21st Century, the editors aim to replicate the tradition of the fruitful Girona Workshops and Seminars, held at the University of Girona, Italy, annually for many years, which offered important scientific gatherings focusing on quantum chemistry. This volume, like the workshops, showcases a large variety of quantum chemical contributions from different points of view from some of the leading scientists in the field today. This unique volume does not pretend to provide a complete overview of quantum chemistry, but it does provide a broad set of contributions by some of the leading scientists on the field, under the expert editorship of two leaders in the field.