Parallel Implementation of Nonadditive Gaussian Process Potentials for Monte Carlo Simulations.

A strategy is presented to implement Gaussian process potentials in molecular simulations through parallel programming. Attention is focused on the three-body nonadditive energy, though all algorithms extend straightforwardly to the additive energy. The method to distribute pairs and triplets between processes is general to all potentials.

Machine learning for non-additive intermolecular potentials: quantum chemistry to first-principles predictions.

Prediction of thermophysical properties from molecular principles requires accurate potential energy surfaces (PES). We present a widely-applicable method to produce first-principles PES from quantum chemistry calculations. Our approach accurately interpolates three-body non-additive interaction data, using the machine learning technique, Gaussian Processes (GP).

Gaussian process models of potential energy surfaces with boundary optimization.

A strategy is outlined to reduce the number of training points required to model intermolecular potentials using Gaussian processes, without reducing accuracy. An asymptotic function is used at a long range, and the crossover distance between this model and the Gaussian process is learnt from the training data. The results are presented for different implementations of this procedure, known as boundary optimization, across the following dimer systems: CO-Ne, HF-Ne, HF-Na, CO-Ne, and (CO).

Direct observation of long chain enrichment in flow-induced nuclei from molecular dynamics simulations of bimodal blends.

Modelling of flow-induced nucleation in polymers suggest that long chains are enriched in nuclei, relative to their melt concentration. This enrichment has important consequences for the nucleation rate and mechanism, but cannot be directly observed with current experimental techniques. Instead, we ran united atom molecular dynamics simulations of bimodal polyethylene blends, comprising linear chains at a 50 : 50 mix of long (1000 carbon) and short (500-125 carbon) chains, under shear flow.

PolySTRAND Model of Flow-Induced Nucleation in Polymers.

We develop a thermodynamic continuum-level model, polySTRAND, for flow-induced nucleation in polymers suitable for use in computational process modeling. The model's molecular origins ensure that it accounts properly for flow and nucleation dynamics of polydisperse systems and can be extended to include effects of exhaustion of highly deformed chains and nucleus roughness. It captures variations with the key processing parameters, flow rate, temperature, and molecular weight distribution.

Molecular dynamics simulations of crystal nucleation in entangled polymer melts under start-up shear conditions.

Understanding the flow induced crystallisation process is necessary due to its technological relevance to polymer processing. Polymer crystallisation controls the morphology of semi-crystalline polymers and hence the properties of the end product. We perform molecular dynamics simulations of polymer melts consisting of sufficiently entangled linear chains under shear flow.

Active learning in Gaussian process interpolation of potential energy surfaces.

Three active learning schemes are used to generate training data for Gaussian process interpolation of intermolecular potential energy surfaces. These schemes aim to achieve the lowest predictive error using the fewest points and therefore act as an alternative to the status quo methods involving grid-based sampling or space-filling designs like Latin hypercubes (LHC). Results are presented for three molecular systems: CO-Ne, CO-H, and Ar.

Interpolation of intermolecular potentials using Gaussian processes.

A procedure is proposed to produce intermolecular potential energy surfaces from limited data. The procedure involves generation of geometrical configurations using a Latin hypercube design, with a maximin criterion, based on inverse internuclear distances. Gaussian processes are used to interpolate the data, using over-specified inverse molecular distances as covariates, greatly improving the interpolation.

Molecular simulation of the thermophysical properties and phase behaviour of impure CO relevant to CCS.

Impurities from the CCS chain can greatly influence the physical properties of CO. This has important design, safety and cost implications for the compression, transport and storage of CO. There is an urgent need to understand and predict the properties of impure CO to assist with CCS implementation.

A Novel Method of Extraction of Blend Component Structure from SANS Measurements of Homopolymer Bimodal Blends.

A new method is presented for the extraction of single-chain form factors and interchain interference functions from a range of small-angle neutron scattering (SANS) experiments on bimodal homopolymer blends. The method requires a minimum of three blends, made up of hydrogenated and deuterated components with matched degree of polymerization at two different chain lengths, but with carefully varying deuteration levels. The method is validated through an experimental study on polystyrene homopolymer bimodal blends with [Formula: see text].

Modelling flow-induced crystallisation in polymers.

Flow profoundly influences the crystallisation kinetics and morphology of polymeric materials. By distorting the configuration of polymer chains, flow breaks down the kinetic barriers to crystallisation and directs the resulting crystallisation. This flow-induced crystallisation (FIC) in polymers is a fascinating, externally driven, non-equilibrium phase transition, which is controlled by kinetics.

A continuum model of cell proliferation and nutrient transport in a perfusion bioreactor.

Tissue engineering aims to regenerate, repair or replace organs or defective tissues. This tissue regeneration often occurs in a bioreactor. Important challenges in tissue engineering include ensuring adequate nutrient supply, maintaining the desired cell distribution and achieving sufficiently high cell yield.

A fast algorithm for simulating flow-induced nucleation in polymers.

We present a fast computer simulation algorithm for high dimensional barrier crossing simulations. The algorithm is described with reference to the Graham and Olmsted (GO) model of flow-induced nucleation in polymers [R. S.

Coarse-grained simulations of stretching entangled DNA using oscillating electric fields.

DNA stretching in entangling media with oscillating electric fields is useful in DNA sequencing. We propose that stretching occurs around hairpin loops at the chain ends, caused by interactions with the medium. Brownian dynamics simulations show that this mechanism explains several experimental observations, including a resonance with varying field frequency.

Kinetic Monte Carlo simulations of flow-induced nucleation in polymer melts.

We derive a kinetic Monte Carlo algorithm to simulate flow-induced nucleation in polymer melts. The crystallisation kinetics are modified by both stretching and orientation of the amorphous chains under flow, which is modelled by a recent non-linear tube theory. Rotation of the crystallites under flow is modelled by a simultaneous Brownian dynamics simulation.

Coarse-grained simulations of flow-induced nucleation in semicrystalline polymers.

We perform kinetic Monte Carlo simulations of flow-induced nucleation in polymer melts with an algorithm that is tractable even at low undercooling. The configuration of the noncrystallized chains under flow is computed with a recent nonlinear tube model. Our simulations predict both enhanced nucleation and the growth of shish-like elongated nuclei for sufficiently fast flows.