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Machine Learning Meets Quantum Physics
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 bottlenec...
Quantum Chemistry in the Age of Machine Learning
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 ...
Advances in Neural Information Processing Systems 15
Proceedings of the 2002 Neural Information Processing Systems Conference.
The Gaussian Approximation Potential
Simulation of materials at the atomistic level is an important tool in studying microscopic structures and processes. The atomic interactions necessary for the simulations are correctly described by Quantum Mechanics, but the size of systems and the length of processes that can be modelled are still limited. The framework of Gaussian Approximation Potentials that is developed in this thesis allows us to generate interatomic potentials automatically, based on quantum mechanical data. The resulting potentials offer several orders of magnitude faster computations, while maintaining quantum mechanical accuracy. The method has already been successfully applied for semiconductors and metals.
Machine Learning
Presents carefully selected supervised and unsupervised learning methods from basic to state-of-the-art,in a coherent statistical framework.
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 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. Wit...
Chemistry of Semiconductors
Silicon, germanium, and compound semiconductors, among which silicon carbide, gallium arsenide and gallium nitride are the most representative examples, play a withstanding role in the world economy, since they were and still are the keys for the advancement of modern microelectronics and optoelectronics, with a wealth of sister technologies relevant for renewable energy solutions and advanced spectroscopy applications. This textbook will cover the synthesis, spectroscopic characterisation and optimisation of semiconductor materials, accounting for the most recent developments in the field of nanomaterials. It will be of great interest for scholars and instructors to have the chance to look ...
Machine Learning in Chemistry
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
Surface Engineering of Graphene and Graphene Quantum Dots for Industrial and Medical Applications
This book explores the synthesis, characterization, and applications of graphene and its derivatives. It covers advancements in improving graphene quality, surface engineering methods, and increasing material functionality. The topics covered include functionalized graphene, graphene quantum dots, novel device fabrication approaches, and diverse applications. The book also investigates the fundamental principles of characterizing graphene and its derivatives, along with electronic structures, theoretical investigations, and computational analyses relevant to their applications, synthesis, and properties. The chapters are organized to cover these topics, starting with a general overview of surface chemistry and its concepts for surface engineering of graphene, the fundamental properties of graphene and its derivatives, their synthesis, and applications in numerous fields, and concludes with a future perspective. Significantly, for the first time, both industrial and medical applications are gathered in one book, enabling us to discuss the confrontation of medical and industrial applications of graphene and graphene quantum dots.
Many-Electron Approaches in Physics, Chemistry and Mathematics
This book provides a broad description of the development and (computational) application of many-electron approaches from a multidisciplinary perspective. In the context of studying many-electron systems Computer Science, Chemistry, Mathematics and Physics are all intimately interconnected. However, beyond a handful of communities working at the interface between these disciplines, there is still a marked separation of subjects. This book seeks to offer a common platform for possible exchanges between the various fields and to introduce the reader to perspectives for potential further developments across the disciplines. The rapid advances of modern technology will inevitably require substantial improvements in the approaches currently used, which will in turn make exchanges between disciplines indispensable. In essence this book is one of the very first attempts at an interdisciplinary approach to the many-electron problem.
