Machine Learning Meets Quantum Physics
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Machine Learning Meets Quantum Physics
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Author : Kristof T. Schütt
language : en
Publisher: Springer Nature
Release Date : 2020-06-03
Machine Learning Meets Quantum Physics written by Kristof T. Schütt and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-03 with Science categories.
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 Machine Learning An Applied Approach
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Author : Santanu Ganguly
language : en
Publisher: Apress
Release Date : 2021-08-11
Quantum Machine Learning An Applied Approach written by Santanu Ganguly and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-11 with Computers categories.
Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into hands-on quantum machine learning (QML) through various options available in industry and research. The first three chapters offer insights into the combination of the science of quantum mechanics and the techniques of machine learning, where concepts of classical information technology meet the power of physics. Subsequent chapters follow a systematic deep dive into various quantum machine learning algorithms, quantum optimization, applications of advanced QML algorithms (quantum k-means, quantum k-medians, quantum neural networks, etc.), qubit state preparation for specific QML algorithms, inference, polynomial Hamiltonian simulation, and more, finishing with advanced and up-to-date research areas such as quantum walks, QML via Tensor Networks, and QBoost. Hands-on exercises from open source libraries regularly used today in industry and research are included, such as Qiskit, Rigetti's Forest, D-Wave's dOcean, Google's Cirq and brand new TensorFlow Quantum, and Xanadu's PennyLane, accompanied by guided implementation instructions. Wherever applicable, the book also shares various options of accessing quantum computing and machine learning ecosystems as may be relevant to specific algorithms. The book offers a hands-on approach to the field of QML using updated libraries and algorithms in this emerging field. You will benefit from the concrete examples and understanding of tools and concepts for building intelligent systems boosted by the quantum computing ecosystem. This work leverages the author’s active research in the field and is accompanied by a constantly updated website for the book which provides all of the code examples. What You will Learn Understand and explore quantum computing and quantum machine learning, and their application in science and industry Explore various data training models utilizing quantum machine learning algorithms and Python libraries Get hands-on and familiar with applied quantum computing, including freely available cloud-based access Be familiar with techniques for training and scaling quantum neural networks Gain insight into the application of practical code examples without needing to acquire excessive machine learning theory or take a quantum mechanics deep dive Who This Book Is For Data scientists, machine learning professionals, and researchers
Molecular Representations For Machine Learning
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Author : Grier M. Jones
language : en
Publisher: American Chemical Society
Release Date : 2023-05-19
Molecular Representations For Machine Learning written by Grier M. Jones and has been published by American Chemical Society this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-19 with Computers categories.
This primer helps the reader understand the basic categories of molecular representations and provides computational tools to generate molecular descriptors in each of these categories. After reading this primer, you will be able to use various methods to generate machine and/or human interpretable representations of molecular systems for inputs to machine learning models or for general chemical data science applications.
Ai Frameworks And Tools For Software Development
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Author : Patel, Rahul K.
language : en
Publisher: IGI Global
Release Date : 2025-04-29
Ai Frameworks And Tools For Software Development written by Patel, Rahul K. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-29 with Computers categories.
The rapid advancements in artificial intelligence (AI) are transforming how organizations approach software development, creating both opportunities and challenges in the workplace. As AI tools become more mainstream, understanding their role, as well as the responsibilities of users, is crucial for ensuring their effective integration into software development processes. A clear framework for introducing AI in Information Systems Management can significantly enhance the efficiency and effectiveness of development teams and their external stakeholders. AI Frameworks and Tools for Software Development presents the best practices, research findings, and guidelines for using AI frameworks and tools in software development. It provides a holistic understanding of these key processes, functions, and workflows that are essential for effective Software Development Lifecycle (SDLC). Covering topics such as industrial automation, knowledge management, and code reusability, this book is an excellent resource for software developers, computer scientists, professionals, researchers, scholars, academicians, and more.
Machine Learning With Quantum Computers
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Author : Maria Schuld
language : en
Publisher: Springer Nature
Release Date : 2021-10-17
Machine Learning With Quantum Computers written by Maria Schuld and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-17 with Science categories.
This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years.
Machine Learning
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Author : Andreas Lindholm
language : en
Publisher: Cambridge University Press
Release Date : 2022-03-31
Machine Learning written by Andreas Lindholm and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-31 with Computers categories.
Presents carefully selected supervised and unsupervised learning methods from basic to state-of-the-art,in a coherent statistical framework.
Molecular Dynamics Probability And Uncertainty
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Author : Peter V. Coveney
language : en
Publisher: Oxford University Press
Release Date : 2025-05-15
Molecular Dynamics Probability And Uncertainty written by Peter V. Coveney and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-15 with Science categories.
Embark on a fascinating exploration of molecular dynamics, which combines the authors' new probabilistic interpretation with cutting-edge simulations, some of which are performed on the largest supercomputers on our planet. From fundamental principles to innovative applications, this book covers the rich tapestry of molecular dynamics and its intersections with biological and medical sciences, materials science and engineering, and artificial intelligence, alongside uncertainty quantification. This enables the authors to highlight the critical role of molecular dynamics in delivering actionable outcomes for drug discovery, materials design and beyond. Beginning with a solid introduction to the intricate world of molecular dynamics, the book goes on to describe its modern probabilistic formulation. It investigates ensemble-based molecular dynamics simulations and free energies, uncovering both the way that ensemble techniques revolutionize simulation methodologies and how they empower researchers to generate new insights. Further, the book explores the exciting realm of simulations for advanced materials and discusses verification, validation and uncertainty quantification, illuminating the synergies between molecular dynamics and artificial intelligence and their potential for transformative breakthroughs. Whether you are a seasoned researcher seeking to expand your knowledge or a curious student eager to investigate the complexities of molecular dynamics, this book serves as an indispensable resource, challenging conventional approaches, offering fresh perspectives and unlocking new insights into real-world problems in this captivating field.
Artificial Intelligence In Catalysis
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Author : Valentine P. Ananikov
language : en
Publisher: John Wiley & Sons
Release Date : 2025-07-02
Artificial Intelligence In Catalysis written by Valentine P. Ananikov and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-02 with Technology & Engineering categories.
Enables researchers and professionals to leverage machine learning tools to optimize catalyst design and chemical processes Artificial Intelligence in Catalysis delivers a state-of-the-art overview of artificial intelligence methodologies applied in catalysis. Divided into three parts, it covers the latest advancements and trends for catalyst discovery and characterization, reaction predictions, and process optimization using machine learning, quantum chemistry, and cheminformatics. Written by an international team of experts in the field, with each chapter combining experimental and computational knowledge, Artificial Intelligence in Catalysis includes information on: Artificial intelligence techniques for chemical reaction monitoring and structural analysis Application of artificial neural networks in the analysis of electron microscopy data Construction of training datasets for chemical reactivity prediction through computational means Catalyst optimization and discovery using machine learning models Predicting selectivity in asymmetric catalysis with machine learning Artificial Intelligence in Catalysis is a practical guide for researchers in academia and industry interested in developing new catalysts, improving organic synthesis, and minimizing waste and energy use.
Computational Technologies In Materials Science
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Author : Shubham Tayal
language : en
Publisher: CRC Press
Release Date : 2021-10-06
Computational Technologies In Materials Science written by Shubham Tayal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-06 with Science categories.
Advanced materials are essential for economic security and human well-being, with applications in industries aimed at addressing challenges in clean energy, national security, and human welfare. Yet, it can take years to move a material to the market after its initial discovery. Computational techniques have accelerated the exploration and development of materials, offering the chance to move new materials to the market quickly. Computational Technologies in Materials Science addresses topics related to AI, machine learning, deep learning, and cloud computing in materials science. It explores characterization and fabrication of materials, machine-learning-based models, and computational intelligence for the synthesis and identification of materials. This book • Covers material testing and development using computational intelligence • Highlights the technologies to integrate computational intelligence and materials science • Details case studies and detailed applications • Investigates challenges in developing and using computational intelligence in materials science • Analyzes historic changes that are taking place in designing materials. This book encourages material researchers and academics to develop novel theories and sustainable computational techniques and explores the potential for computational intelligence to replace traditional materials research.
Advanced Machine Learning With Evolutionary And Metaheuristic Techniques
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Author : Jayaraman Valadi
language : en
Publisher: Springer Nature
Release Date : 2024-04-22
Advanced Machine Learning With Evolutionary And Metaheuristic Techniques written by Jayaraman Valadi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-22 with Mathematics categories.
This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of machine learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency. Or unlock the strategies behind hyperparameter optimization to enhance your transfer learning algorithms, yielding remarkable outcomes. Or embark on an illuminating journey through evolutionary techniques designed for constructing deep-learning frameworks. The book also introduces an intelligent RPL attack detection system tailored for IoT networks. Explore a promising avenue of optimization by fusing Particle Swarm Optimization with Reinforcement Learning. It uncovers the indispensable role of metaheuristics in supervised machine learning algorithms. Ultimately, this book bridges the realms of evolutionary dynamic optimization andmachine learning, paving the way for pioneering innovations in the field.