Download or read book Building Models of Spectroscopy for Condensed Phase Systems with Atomistic Detail Using Theory and Machine Learning written by Michael Stephen Chen and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spectroscopic techniques provide us with a means of investigating a system's microscopic structure and dynamics. Accurate atomistic simulations can help us explicitly connect spectroscopic features to the underlying electronic and nuclear structure and dynamics that give rise to them. In this dissertation, I highlight my work in rendering accurate atomistic simulations of different linear and multidimensional spectroscopies more computationally tractable by leveraging semiclassical approaches for theoretically treating spectroscopies and developing machine learning (ML) models to serve as proxies for ab initio electronic structure calculations. Chapter 1 provides a quick overview of the ML approaches I employed and theoretical background for how I used molecular dynamics (MD) simulations to simulate different spectroscopies. Chapter 2 presents work I have conducted in training ML potential energy surfaces for liquid water using transfer learning to target high-level ab initio electronic structure theories in order to accurately and efficiently conduct MD simulations. In Chapters 3 and 4, I develop ML models for electronic excitation energies in order to simulate linear and 2D electronic absorption spectroscopies for various solvated chromophore systems and highlight the inability of TDDFT to treat the extent to which hydrogen-bonding affects the distribution of excitation energies. Lastly, Chapter 5 highlights my work in developing a theoretical framework to simulate novel time-resolved X-ray diffraction experiments, which can be used to probe the orientational structural dynamics of disordered condensed phase systems, and benchmarking with results for liquid chloroform.
Download or read book Condensed Phase Molecular Spectroscopy and Photophysics written by Anne Myers Kelley and published by John Wiley & Sons. This book was released on 2022-09-27 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Condensed-Phase Molecular Spectroscopy and Photophysics An introduction to one of the fundamental tools in chemical research—spectroscopy and photophysics in condensed-phase and extended systems Condensed-Phase Molecular Spectroscopy and Photophysics comprehensively covers radiation-matter interactions for molecules in condensed phases along with metallic and semiconductor nanostructures, examining optical processes in extended systems such as metals, semiconductors, and conducting polymers and addressing the unique optical properties of nanoscale systems. The text differs from others through its emphasis on the molecule-environment interactions that strongly influence spectra in condensed phases, including spectroscopy and photophysics of molecular aggregates, molecular solids, and metals and semiconductors, as well as more modern topics such as two-dimensional and single-molecule spectroscopy. To aid in reader comprehension, the text includes case studies and illustrated examples. An online manual with solutions to the problems in the book is available to all readers on a companion website. Condensed-Phase Molecular Spectroscopy and Photophysics begins with an introduction to quantum mechanics that sets a solid foundation for understanding the text’s subsequent topics, including: Electromagnetic radiation and radiation-matter interactions, molecular vibrations and infrared spectroscopy, and electronic spectroscopy Photophysical processes and light scattering, nonlinear and pump-probe spectroscopies, and electron transfer processes Basic rotational spectroscopy and statistical mechanics, Raman scattering, 2D and single-molecule spectroscopies, and time-domain pictures of steady-state spectroscopies Time-independent quantum mechanics, statistical mechanics, group theory, radiation-matter interactions, and system-bath interactions Atomic spectroscopy, photophysical processes, light scattering, nonlinear and pump-probe spectroscopies, two-dimensional spectroscopies, and metals and plasmons Written for researchers and upper-level undergraduate and graduate courses in physical and materials chemistry, Condensed-Phase Molecular Spectroscopy and Photophysics is a valuable learning resource that is uniquely designed to equip readers to solve a broad array of current problems and challenges in the vast field of chemistry.
Download or read book Predicting Vibrational Spectra of Condensed Phase Systems written by Martin Brehm and published by . This book was released on 2023* with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Vibrational spectroscopy; analytical chemistry; condensed phase; molecular dynamics; computer simulations; infrared spectroscopy; Raman spectroscopy.
Download or read book A Computational Approach to Spectroscopy of Molecular Systems written by Andrew Davis Horning and published by . This book was released on 2015 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis describes a series of approaches for modeling spectroscopy of molecular systems in aqueous environments, focusing on proton transfer, water dynamics, and hydrogen bonding interactions. The spectroscopy motivating this work ranges from nuclear to vibrational to electronic, spanning from 106 to 1015Hz. The work in this thesis focuses on connecting these spectroscopic measurements directly to the underlying molecular structure through a variety of computational methods. After a discussion of the properties of hydrogen bonds and strongly hydrogen bonded systems, I present a phenomenological approach for modeling linear and nonlinear infrared spectroscopy in condensed phase chemical systems, focusing on applications to strongly hydrogen bonded complexes. In this I also derive and demonstrate the application of a Langevin-like Brownian oscillator model for the bath in computational spectroscopy, utilizing the language of open quantum systems along with the semiclassical approximation for the linear and nonlinear response functions to numerically calculate nonlinear spectra . With this we can examine phenomena previously difficult with other methods, including non-Gaussian dynamics, correlated motions, highly anharmonic potentials, proton transfer, and complex system-bath relationships. Next I describe a design problem reliant on water dynamics and hydrogen bonding: improving and tuning the water enhancement properties of MRI contrast agents. This work focuses specifically on a new ligand architecture with promising modular, tunable synthetic properties. Motivated by the fundamental equations governing relaxivity enhancement, this work proposes systems for which it is possible to improve and tune the molecular rotational timescale, fast water motion, and coordinating water geometry to overcome fundamental limitations in currently available contrast agents. Lastly, this thesis discusses a method of feature selection that works to identify key molecular variables important in influencing the absorption profiles of fluorescent proteins, utilizing machine learning on spectral clusters built from an ensemble of ground state dynamics trajectories. Using the example of green fluorescent protein, I show that this new feature selection protocol can highlight important interactions in the native structure that can help inform rational design of fluorescent proteins.
Download or read book A Chemist s Guide to Density Functional Theory written by Wolfram Koch and published by John Wiley & Sons. This book was released on 2015-11-18 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Chemists familiar with conventional quantum mechanics will applaud and benefit greatly from this particularly instructive, thorough and clearly written exposition of density functional theory: its basis, concepts, terms, implementation, and performance in diverse applications. Users of DFT for structure, energy, and molecular property computations, as well as reaction mechanism studies, are guided to the optimum choices of the most effective methods. Well done!" Paul von Rague Schleyer "A conspicuous hole in the computational chemist's library is nicely filled by this book, which provides a wide-ranging and pragmatic view of the subject.[...It] should justifiably become the favorite text on the subject for practioneers who aim to use DFT to solve chemical problems." J. F. Stanton, J. Am. Chem. Soc. "The authors' aim is to guide the chemist through basic theoretical and related technical aspects of DFT at an easy-to-understand theoretical level. They succeed admirably." P. C. H. Mitchell, Appl. Organomet. Chem. "The authors have done an excellent service to the chemical community. [...] A Chemist's Guide to Density Functional Theory is exactly what the title suggests. It should be an invaluable source of insight and knowledge for many chemists using DFT approaches to solve chemical problems." M. Kaupp, Angew. Chem.
Download or read book Modelling Condensed Phase Systems written by Alexander Harold de Vries and published by . This book was released on 1995 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Quantum Chemistry in the Age of Machine Learning written by Pavlo O. Dral and published by Elsevier. This book was released on 2022-09-16 with total page 702 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Dynamics and Spectra in Condensed Phases written by Younjoon Jung and published by . This book was released on 2002 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, several problems regarding dynamics and spectra in condensed phases are theoretically analyzed via analytical models. The thesis consists of four main topics. First, a theoretical description of single molecule spectroscopy is presented in order to study time-dependent fluctuations of single molecule spectra in a dynamic environment. In particular, the photon counting statistics is investigated for a single molecule undergoing a generic type of spectral diffusion process. An exact analytical solution is found for this case, and various physical limits are analyzed. Second, motivated by recent experimental observations of anomalous spectral fluctuations in quantum dot systems, both the lineshape phenomenon and the photon counting statistics are explored when spectral fluctuations are characterized by power-law statistics, for which there is no finite timescale. Unique features of the power-law statistics are demonstrated in spectral properties of those systems. Third, a spectral analysis method is developed for the non-adiabatic electron transfer reactions, which allows a unified treatment of diverse kinetic regimes in the electron transfer process. The method is applied to electron transfer reactions in mixed-valence systems in order to explore the possibility of electronic coherence. Finally, effects of the nonequilibrium bath relaxation on the excitation energy transfer process are investigated by generalizing the Forster-Dexter theory of excitation energy transfer to the case of the nonstationary bath relaxation.
Download or read book Dynamical Spectroscopy written by James Brian Almy and published by . This book was released on 1999 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Machine Learning Meets Quantum Physics written by Kristof T. Schütt and published by Springer Nature. This book was released on 2020-06-03 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Condensed Matter Field Theory written by Alexander Altland and published by Cambridge University Press. This book was released on 2010-03-11 with total page 785 pages. Available in PDF, EPUB and Kindle. Book excerpt: This primer is aimed at elevating graduate students of condensed matter theory to a level where they can engage in independent research. Topics covered include second quantisation, path and functional field integration, mean-field theory and collective phenomena.
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1994 with total page 836 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Machine Learning in Chemistry written by Hugh M. Cartwright and published by Royal Society of Chemistry. This book was released on 2020-07-15 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Machine Learning in Chemistry written by Edward O. Pyzer-Knapp and published by . This book was released on 2020-10-22 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Energy Landscapes written by David Wales and published by Cambridge University Press. This book was released on 2003 with total page 696 pages. Available in PDF, EPUB and Kindle. Book excerpt: A self-contained account of energy landscape theory aimed at graduate students and researchers.
Download or read book Hands On Mathematics for Deep Learning written by Jay Dawani and published by Packt Publishing Ltd. This book was released on 2020-06-12 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook Description Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL. What you will learnUnderstand the key mathematical concepts for building neural network modelsDiscover core multivariable calculus conceptsImprove the performance of deep learning models using optimization techniquesCover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizerUnderstand computational graphs and their importance in DLExplore the backpropagation algorithm to reduce output errorCover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs)Who this book is for This book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.
Download or read book An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation written by Gregory R. Bowman and published by Springer Science & Business Media. This book was released on 2013-12-02 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this book volume is to explain the importance of Markov state models to molecular simulation, how they work, and how they can be applied to a range of problems. The Markov state model (MSM) approach aims to address two key challenges of molecular simulation: 1) How to reach long timescales using short simulations of detailed molecular models. 2) How to systematically gain insight from the resulting sea of data. MSMs do this by providing a compact representation of the vast conformational space available to biomolecules by decomposing it into states sets of rapidly interconverting conformations and the rates of transitioning between states. This kinetic definition allows one to easily vary the temporal and spatial resolution of an MSM from high-resolution models capable of quantitative agreement with (or prediction of) experiment to low-resolution models that facilitate understanding. Additionally, MSMs facilitate the calculation of quantities that are difficult to obtain from more direct MD analyses, such as the ensemble of transition pathways. This book introduces the mathematical foundations of Markov models, how they can be used to analyze simulations and drive efficient simulations, and some of the insights these models have yielded in a variety of applications of molecular simulation.
