Books on the topic 'Non-equilibrium structures'

This book is the first to detail the chemical changes that occur in deforming materials subjected to unequal compressions. While thermodynamics provides, at the macroscopic level, an excellent means of understanding and predicting the behavior of materials in equilibrium and non-equilibrium states, much less is understood about nonhydrostatic stress and interdiffusion at the chemical level. Little is known, for example, about the chemistry of a state resulting from a cylinder of deforming material being more strongly compressed along its length than radially, a state of non-equilibrium that remains no matter how ideal the cylinder's condition in other respects. M. Brian Bayly here provides the outline of a comprehensive approach to gaining a simplified and unified understanding of such phenomena. The author's perspective differs from those commonly found in the technical literature in that he emphasizes two little-used equations that allow for a description and clarification of viscous deformation at the chemical level. Written at a level that will be accessible to many non-specialists, this book requires only a fundamental understanding of elementary mathematics, the nonhydrostatic stress state, and chemical potential. Geochemists, petrologists, structural geologists, and materials scientists will find Chemical Change in Deforming Materials interesting and useful.

This book focuses on a method for exploring, explaining, and understanding the behavior of large many-body systems. It describes an approach to non-equilibrium behavior that focuses on structures (represented by correlation functions) that characterize mesoscale properties of the systems. In other words, rather than a fully bottom-up approach, starting with the components at the atomic or molecular scale, the “hydrodynamic approach” aims to describe and account for continuum behaviors by largely ignoring details at the “fundamental” level. This methodological approach has its origins in Einstein’s work on Brownian motion. He gave what may be the first instance of “upscaling” to determine an effective (continuum) value for a material parameter—the viscosity. His method is of a kind with much work in the science of materials. This connection and the wide-ranging interdisciplinary nature of these methods are stressed. Einstein also provided the first expression of a fundamental theorem of statistical mechanics called the Fluctuation-Dissipation theorem. This theorem provides the primary justification for the hydrodynamic, mesoscale methodology. Philosophical consequences include an argument to the effect that mesoscale parameters can be the natural variables for characterizing many-body systems. Further, the book offers a new argument for why continuum theories (fluid mechanics and equations for the bending of beams) are still justified despite completely ignoring the fact that fluids and materials have lower scale structure. The book argues for a middle way between continuum theories and atomic theories. A proper understanding of those connections can be had when mesoscales are taken seriously.

This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic potential functions using NNs; (v) self-starting methods for obtaining analytic PES from ab inito electronic structure calculations using direct dynamics; (vi) development of a novel method, namely, combined function derivative approximation (CFDA) for simultaneous fitting of a PES and its corresponding force fields using feedforward neural networks; (vii) development of generalized PES using many-body expansions, NNs, and moiety energy approximations; (viii) NN methods for data analysis, reaction probabilities, and statistical error reduction in chemical reaction dynamics; (ix) accurate prediction of higher-level electronic structure energies (e.g. MP4 or higher) for large databases using NNs, lower-level (Hartree-Fock) energies, and small subsets of the higher-energy database; and finally (x) illustrative examples of NN applications to chemical reaction dynamics of increasing complexity starting from simple near equilibrium structures (vibrational state studies) to more complex non-adiabatic reactions. The monograph is prepared by an interdisciplinary group of researchers working as a team for nearly two decades at Oklahoma State University, Stillwater, OK with expertise in gas phase reaction dynamics; neural networks; various aspects of MD and Monte Carlo (MC) simulations of nanometric cutting, tribology, and material properties at nanoscale; scaling laws from atomistic to continuum; and neural networks applications to chemical reaction dynamics. It is anticipated that this emerging field of NN in chemical reaction dynamics will play an increasingly important role in MD, MC, and quantum mechanical studies in the years to come.

This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic potential functions using NNs; (v) self-starting methods for obtaining analytic PES from ab inito electronic structure calculations using direct dynamics; (vi) development of a novel method, namely, combined function derivative approximation (CFDA) for simultaneous fitting of a PES and its corresponding force fields using feedforward neural networks; (vii) development of generalized PES using many-body expansions, NNs, and moiety energy approximations; (viii) NN methods for data analysis, reaction probabilities, and statistical error reduction in chemical reaction dynamics; (ix) accurate prediction of higher-level electronic structure energies (e.g. MP4 or higher) for large databases using NNs, lower-level (Hartree-Fock) energies, and small subsets of the higher-energy database; and finally (x) illustrative examples of NN applications to chemical reaction dynamics of increasing complexity starting from simple near equilibrium structures (vibrational state studies) to more complex non-adiabatic reactions. The monograph is prepared by an interdisciplinary group of researchers working as a team for nearly two decades at Oklahoma State University, Stillwater, OK with expertise in gas phase reaction dynamics; neural networks; various aspects of MD and Monte Carlo (MC) simulations of nanometric cutting, tribology, and material properties at nanoscale; scaling laws from atomistic to continuum; and neural networks applications to chemical reaction dynamics. It is anticipated that this emerging field of NN in chemical reaction dynamics will play an increasingly important role in MD, MC, and quantum mechanical studies in the years to come.

In some cases of insurgency, the combat frontier is contested and erratic, as rebels target cities as their economic prey. In other cases, it is tidy and stable, seemingly representing an equilibrium in which cities are effectively protected from violent non-state actors. What factors account for these differences in the interface urban-based states and rural-based challengers? To explore this question, this book examines two regions representing two dramatically different outcomes. In West Africa (Liberia and Sierra Leone), capital cities became economic targets for rebels, who posed dire threats to the survival of the state. In Maoist India, despite an insurgent ideology aiming to overthrow the state via a strategy of progressive city capture, the combat frontier effectively firewalls cities from Maoist violence. This book argues that trade networks underpinning the economic relationship between rural and urban areas—termed “interstitial economies”—may differ dramatically in their impact on (and response to) the combat frontier. It explains rebel predatory tendencies toward cities as a function of transport networks allowing monopoly profits to be made by urban-based traders. It explains combat frontier delineation as a function of the social structure of the trade networks: hierarchical networks permit elite–elite bargains that cohere the frontier. These factors represent what might be termed respectively the “hardware” and “software” of the rural–urban economic relationship. Of interest to any student of political economy and violence, this book presents new arguments and insights about the relationships between violence and the economy, predation and production, core and periphery.