6.2.2. Definition of Effective Properties 3064 6.3. Response Properties to Magnetic Fields 3066 6.3.1. Nuclear Shielding 3066 6.3.2. Indirect Spin−Spin Coupling 3067 6.3.3. EPR Parameters 3068 6.4. Properties of Chiral Systems 3069 6.4.1. Electronic Circular Dichroism (ECD) 3069 6.4.2. Optical Rotation (OR) 3069 6.4.3. VCD and VROA 3070 7. Continuum and Discrete Models 3071 7.1. Continuum Methods within MD and MC Simulations 3072

/pdf/quantum-mechanical-continuum-solvation-models-3edv963g14.pdf

Quantum mechanical continuum solvation models.

https://zenodo.org/record/1258475/files/article.pdf

Electron transfers in chemistry and biology

From the Publisher:
Artificial "neural networks" are now widely used as flexible models for regression classification applications, but questions remain regarding what these models mean, and how they can safely be used when training data is limited. Bayesian Learning for Neural Networks shows that Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional neural network learning methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. Use of these models in practice is made possible using Markov chain Monte Carlo techniques. Both the theoretical and computational aspects of this work are of wider statistical interest, as they contribute to a better understanding of how Bayesian methods can be applied to complex problems. Presupposing only the basic knowledge of probability and statistics, this book should be of interest to many researchers in statistics, engineering, and artificial intelligence. Software for Unix systems that implements the methods described is freely available over the Internet.

Bayesian learning for neural networks

Hamiltonian dynamics can be used to produce distant proposals for the Metropolis algorithm, thereby avoiding the slow exploration of the state space that results from the diffusive behaviour of simple random-walk proposals. Though originating in physics, Hamiltonian dynamics can be applied to most problems with continuous state spaces by simply introducing fictitious "momentum" variables. A key to its usefulness is that Hamiltonian dynamics preserves volume, and its trajectories can thus be used to define complex mappings without the need to account for a hard-to-compute Jacobian factor - a property that can be exactly maintained even when the dynamics is approximated by discretizing time. In this review, I discuss theoretical and practical aspects of Hamiltonian Monte Carlo, and present some of its variations, including using windows of states for deciding on acceptance or rejection, computing trajectories using fast approximations, tempering during the course of a trajectory to handle isolated modes, and short-cut methods that prevent useless trajectories from taking much computation time.

MCMC using Hamiltonian dynamics

Dynamic strength of molecular adhesion bonds.

Simulation of the Smoluchowski dynamics of ions in solution has been applied to charged soft-sphere models for aqueous NaCl and CuS04. In these models the experimental ionic mobilities at infinite dilution also are specified while, in this initial study, the hydrodynamic interactions among the ions are suppressed. The simulations show the large effect of the Maxwell (or Debye-Falkenhagen) relaxation on the time-dependence of the mean square displacements of the ions. We illustrate the use of the resulting set of selfand distinct diffusion coefficients to test a recent theory.

Brownian Dynamics Simulation of Models for Aqueous 1M NaCl and 1/2M CuSO4

Enthalpies of alkylammonium ions in water, heavy water, propylene carbonate, and dimethyl sulfoxide.

The model to which the cluster theory of conductance was applied in an earlier paper is extended to allow for nonpairwise contributions to the interactions among the ions When the nonpairwise parts of the long‐range hydrodynamic interactions are included in the cluster theory calculation, then the electrophoretic term of the limiting law found by Debye, Huckel, and Onsager is recovered

Harold L. Friedman

Papers

Brownian Dynamics Simulation of Models for Aqueous 1M NaCl and 1/2M CuSO4

Enthalpies of alkylammonium ions in water, heavy water, propylene carbonate, and dimethyl sulfoxide.

On the Electrophoretic Term of the Limiting Law for Electrical Conductance in Solutions

An interaction-site representation of the dynamic structure factor of liquid and the solvation dynamics

Application of the scaled particle theory to aqueous solutions of nonpolar gases