This thesis proposes useful tools, on-the-fly trajectory mapping method and Reaction Space Projector (ReSPer), to analyze chemical reaction mechanisms by combining the reaction route map and the ab initio molecular dynamics. The key concept for the proposed tools is the Cartesian distance between pairwise molecular structures, and a practical procedure to get the optimal distance is introduced. The on-the-fly trajectory mapping method tracks the distance function between reference structures and molecular structures along the trajectory. Although this method provides fruitful insight into dynamic reaction behaviors, the visualization of reaction routes into a low-dimensional space is still challenging because of the multi-dimensionality. ReSPer successfully constructs a low-dimensional reaction space defined by mathematically-selected principal coordinates representing mutual distance relationships in the full-dimensional space. ReSPer also enables us to project trajectories into the reaction space in the reduced dimension. In this thesis, these methods are applied to several reactions, including bifurcating and photochemical reactions, revealing dynamically-allowed reaction mechanisms. This thesis provides robust and versatile tools to elucidate dynamical reaction routes on the basis of the reduced-dimensionality reaction route map and will help control chemical reaction dynamics and select descriptors for machine learning.

This volume presents the current status of software development in the field of computational and theoretical chemistry and gives an overview of the emerging trends. The challenges of maintaining the legacy codes and their adaptation to the rapidly growing hardware capabilities and the new programming environments are surveyed in a series of topical reviews written by the core developers and maintainers of the popular quantum chemistry and molecular dynamics programs. Special emphasis is given to new computational methodologies and practical aspects of their implementation and application in the computational chemistry codes. Modularity of the computational chemistry software is an emerging concept that enables to bypass the development and maintenance bottleneck of the legacy software and to customize the software using the best available computational procedures implemented in the form of self-contained modules. Perspectives on modular design of the computer programs for modeling molecular electronic structure, non-adiabatic dynamics, kinetics, as well as for data visualization are presented by the researchers actively working in the field of software development and application. This volume is of interest to quantum and computational chemists as well as experimental chemists actively using and developing computational software for their research. Chapters "MLatom 2: An Integrative Platform for Atomistic Machine Learning” and “Evolution of the Automatic Rhodopsin Modeling (ARM) Protocol" are available open access under a CC BY 4.0 License via link.springer.com.

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

Ab initio molecular dynamics revolutionized the field of realistic computer simulation of complex molecular systems and processes, including chemical reactions, by unifying molecular dynamics and electronic structure theory. This book provides the first coherent presentation of this rapidly growing field, covering a vast range of methods and their applications, from basic theory to advanced methods. This fascinating text for graduate students and researchers contains systematic derivations of various ab initio molecular dynamics techniques to enable readers to understand and assess the merits and drawbacks of commonly used methods. It also discusses the special features of the widely used Car–Parrinello approach, correcting various misconceptions currently found in research literature. The book contains pseudo-code and program layout for typical plane wave electronic structure codes, allowing newcomers to the field to understand commonly used program packages and enabling developers to improve and add new features in their code.

This is the first book to present the necessary quantum chemical methods for both resonance types in one handy volume, emphasizing the crucial interrelation between NMR and EPR parameters from a computational and theoretical point of view. Here, readers are given a broad overview of all the pertinent topics, such as basic theory, methodic considerations, benchmark results and applications for both spectroscopy methods in such fields as biochemistry, bioinorganic chemistry as well as with different substance classes, including fullerenes, zeolites and transition metal compounds. The chapters have been written by leading experts in a given area, but with a wider audience in mind. The result is the standard reference on the topic, serving as a guide to the best computational methods for any given problem, and is thus an indispensable tool for scientists using quantum chemical calculations of NMR and EPR parameters. A must-have for all chemists, physicists, biologists and materials scientists who wish to augment their research by quantum chemical calculations of magnetic resonance data, but who are not necessarily specialists in these methods or their applications. Furthermore, specialists in one of the subdomains of this wide field will be grateful to find here an overview of what lies beyond their own area of focus.

Molecular Modeling of Proteins, Second Edition provides a theoretical background of various methods available and enables non-specialists to apply methods to their problems by including updated chapters and new material not covered in the first edition. This detailed volume opens by featuring classical and advanced simulation methods as well as methods to set-up complex systems such as lipid membranes and membrane proteins and continues with chapters devoted to the simulation and analysis of conformational changes of proteins, computational methods for protein structure prediction, usage of experimental data in combination with computational techniques, as well as protein-ligand interactions, which are relevant in the drug design process. Written for the highly successful Methods in Molecular Biology series, chapters include thorough introductions, step-by-step instructions and notes on troubleshooting and avoiding common pitfalls. Update-to-date and authoritative, Molecular Modeling of Proteins, Second Edition aims to aid researchers in the physical, chemical and biosciences interested in utilizing this powerful technology.