Continuum Theory of Polymer Crystallization

We present a kinetic model of crystal growth of polymers of finite molecular weight. Experiments help to classify polymer crystallization broadly into two kinetic regimes. One is observed in melts or in high molar mass polymer solutions and is dominated by nucleation control with $G \sim \exp(1/T \Delta T)$, where $G$ is the growth rate and $\Delta T$ is the super-cooling. The other is observed in low molar mass solutions (as well as for small molecules) and is diffusion controlled with $G \sim \Delta T$, for small $\Delta T$. Our model unifies these two regimes in a single formalism. The model accounts for the accumulation of polymer chains near the growth front and invokes an entropic barrier theory to recover both limits of nucleation and diffusion control. The basic theory applies to both melts and solutions, and we numerically calculate the growth details of a single crystal in a dilute solution. The effects of molecular weight and concentration are also determined considering conventional polymer dynamics. Our theory shows that entropic considerations, in addition to the traditional energetic arguments, can capture general trends of a vast range of phenomenology. Unifying ideas on crystallization from small molecules and from flexible polymer chains emerge from our theory.Comment: 37 double-spaced pages including 8 figures, submitted to the Journal of Chemical Physic

On the Kauffman bracket skein module of the quaternionic manifold

We use recoupling theory to study the Kauffman bracket skein module of the quaternionic manifold over Z[A,A^{-1}] localized by inverting all the cyclotomic polynomials. We prove that the skein module is spanned by five elements. Using the quantum invariants of these skein elements and the Z_2 homology of the manifold, we determine that they are linearly independent.Comment: corrected summation signs in figures 14, 15, 17. Other minor change

Microstructure and velocity of field-driven solid-on-solid interfaces moving under stochastic dynamics with local energy barriers

We study the microscopic structure and the stationary propagation velocity of (1+1)-dimensional solid-on-solid interfaces in an Ising lattice-gas model, which are driven far from equilibrium by an applied force, such as a magnetic field or a difference in (electro)chemical potential. We use an analytic nonlinear-response approximation [P.A. Rikvold and M. Kolesik, J. Stat. Phys. 100, 377 (2000)] together with kinetic Monte Carlo simulations. Here we consider interfaces that move under Arrhenius dynamics, which include a microscopic energy barrier between the allowed Ising/lattice-gas states. Two different dynamics are studied: the standard one-step dynamic (OSD) [H.C. Kang and W. Weinberg, J. Chem. Phys. 90, 2824 (1992)] and the two-step transition-dynamics approximation (TDA) [T. Ala-Nissila, J. Kjoll, and S.C. Ying, Phys. Rev. B 46, 846 (1992)]. In the OSD the effects of the applied force and the interaction energies in the model factorize in the transition rates (a soft dynamic), while in the TDA such factorization is not possible (a hard dynamic). In full agreement with previous general theoretical results we find that the local interface width under the TDA increases dramatically with the applied force. In contrast, the interface structure with the OSD is only weakly influenced by the force, in qualitative agreement with the theoretical expectations. Results are also obtained for the force-dependence and anisotropy of the interface velocity, which also show differences in good agreement with the theoretical expectations for the differences between soft and hard dynamics. Our results confirm that different stochastic interface dynamics that all obey detailed balance and the same conservation laws nevertheless can lead to radically different interface responses to an applied force.Comment: 18 pages RevTex. Minor revisions. Phys. Rev. B, in pres

Spiral Evolution in a Confined Geometry

Supported nanoscale lead crystallites with a step emerging from a non-centered screw dislocation on the circular top facet were prepared by rapid cooling from just above the melting temperature. STM observations of the top facet show a nonuniform rotation rate and shape of the spiral step as the crystallite relaxes. These features can be accurately modeled using curvature driven dynamics, as in classical models of spiral growth, with boundary conditions fixing the dislocation core and regions of the step lying along the outer facet edge.Comment: 4 pages, 3 figures, to be published in Physical Review Letter

Expert-augmented machine learning.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications

First-principles calculation of intrinsic defect formation volumes in silicon

We present an extensive first-principles study of the pressure dependence of the formation enthalpies of all the know vacancy and self-interstitial configurations in silicon, in each charge state from -2 through +2. The neutral vacancy is found to have a formation volume that varies markedly with pressure, leading to a remarkably large negative value (-0.68 atomic volumes) for the zero-pressure formation volume of a Frenkel pair (V + I). The interaction of volume and charge was examined, leading to pressure--Fermi level stability diagrams of the defects. Finally, we quantify the anisotropic nature of the lattice relaxation around the neutral defects.Comment: 9 pages, 9 figure

Monte Carlo with Absorbing Markov Chains: Fast Local Algorithms for Slow Dynamics

A class of Monte Carlo algorithms which incorporate absorbing Markov chains is presented. In a particular limit, the lowest-order of these algorithms reduces to the $n$-fold way algorithm. These algorithms are applied to study the escape from the metastable state in the two-dimensional square-lattice nearest-neighbor Ising ferromagnet in an unfavorable applied field, and the agreement with theoretical predictions is very good. It is demonstrated that the higher-order algorithms can be many orders of magnitude faster than either the traditional Monte Carlo or $n$-fold way algorithms.Comment: ReVTeX, Request 3 figures from [email protected]

Full-Process Computer Model of Magnetron Sputter, Part I: Test Existing State-of-Art Components

This work is part of a larger project to develop a modeling capability for magnetron sputter deposition. The process is divided into four steps: plasma transport, target sputter, neutral gas and sputtered atom transport, and film growth, shown schematically in Fig. 1. Each of these is simulated separately in this Part 1 of the project, which is jointly funded between CMLS and Engineering. The Engineering portion is the plasma modeling, in step 1. The plasma modeling was performed using the Object-Oriented Particle-In-Cell code (OOPIC) from UC Berkeley [1]. Figure 2 shows the electron density in the simulated region, using magnetic field strength input from experiments by Bohlmark [2], where a scale of 1% is used. Figures 3 and 4 depict the magnetic field components that were generated using two-dimensional linear interpolation of Bohlmark's experimental data. The goal of the overall modeling tool is to understand, and later predict, relationships between parameters of film deposition we can change (such as gas pressure, gun voltage, and target-substrate distance) and key properties of the results (such as film stress, density, and stoichiometry.) The simulation must use existing codes, either open-source or low-cost, not develop new codes. In part 1 (FY07) we identified and tested the best available code for each process step, then determined if it can cover the size and time scales we need in reasonable computation times. We also had to determine if the process steps are sufficiently decoupled that they can be treated separately, and identify any research-level issues preventing practical use of these codes. Part 2 will consider whether the codes can be (or need to be) made to talk to each other and integrated into a whole

Size and habit evolution of PETN crystals - a lattice Monte Carlo study

Starting from an accurate inter-atomic potential we develop a simple scheme of generating an ''on-lattice'' molecular potential of short range, which is then incorporated into a lattice Monte Carlo code for simulating size and shape evolution of nanocrystallites. As a specific example, we test such a procedure on the morphological evolution of a molecular crystal of interest to us, e.g., Pentaerythritol Tetranitrate, or PETN, and obtain realistic facetted structures in excellent agreement with experimental morphologies. We investigate several interesting effects including, the evolution of the initial shape of a ''seed'' to an equilibrium configuration, and the variation of growth morphology as a function of the rate of particle addition relative to diffusion