Accelerating Solvent Dynamics with Replica Exchange for Improved Free Energy Sampling

Molecular reactions in solution typically involve solvent exchange; for example, a surface must partly desolvate for a molecule to adsorb onto it. When these reactions are simulated, slow solvent dynamics can limit the sampling of configurations and reduce the accuracy of free energy estimates. Here, we combine Hamiltonian replica exchange (HREX) with well-tempered metadynamics (WTMD) to accelerate the sampling of solvent configurations orthogonal to the collective variable space. We compute the formation free energy of a carbonate vacancy in the calcite–water interface and find that the combination of WTMD with HREX significantly improves the sampling relative to WTMD without HREX

Nonequilibrium capture of impurities that completely block kinks during crystal growth

Some impurities cannot integrate into isolated kinks because they completely block the growth of the kinks to which they adsorb. For this class of impurity, we derive an equation for the amount that incorporates into a crystal during growth of the elementary step by assuming that such an impurity incorporates if and only if it gets captured between a kink and an antikink. We show that the impurity concentration in the crystal increases monotonically with the impurity concentration in the mother phase, but that it can vary non-monotonically with both the supersaturation of the mother phase and the kink density of the step. In contrast to other capture mechanisms, we find that weakly adsorbed impurities incorporate to an extent that is independent of the supersaturation when the supersaturation is high. Irrespective of the growth conditions, the amount of impurity that can incorporate into a crystal is limited by an upper bound determined by the kink density

Calcite Kinks Grow via a Multistep Mechanism

The classical model of crystal growth assumes that kinks grow via a sequence of independent adsorption events where each solute transitions from the solution directly to the crystal lattice site. Here, we challenge this view by showing that some calcite kinks grow via a multistep mechanism where the solute adsorbs to an intermediate site and only transitions to the lattice site upon the adsorption of a second solute. We compute the free energy curves for Ca and CO3 ions adsorbing to a large selection of kink types, and we identify kinks terminated both by Ca ions and by CO3 ions that grow in this multistep way

Simulating electronically driven structural changes in silicon with two-temperature molecular dynamics

Radiation can drive the electrons in a material out of thermal equilibrium with the nuclei, producing hot, transient electronic states that modify the interatomic potential energy surface. We present a rigorous formulation of two-temperature molecular dynamics that can accommodate these electronic effects in the form of electronic-temperature-dependent force fields. Such a force field is presented for silicon, which has been constructed to reproduce the ab initio-derived thermodynamics of the diamond phase for electronic temperatures up to 2.5eV, as well as the structural dynamics observed experimentally under nonequilibrium conditions in the femtosecond regime. This includes nonthermal melting on a subpicosecond timescale to a liquidlike state for electronic temperatures above ∼1eV. The methods presented in this paper lay a rigorous foundation for the large-scale atomistic modeling of electronically driven structural dynamics with potential applications spanning the entire domain of radiation damage

Calcite Kinetics for Spiral Growth and Two-Dimensional Nucleation

Calcite crystals grow by means of molecular steps that develop on {10.4} faces. These steps can arise stochastically via two-dimensional (2D) nucleation or emerge steadily from dislocations to form spiral hillocks. Here, we determine the kinetics of these two growth mechanisms as a function of supersaturation. We show that calcite crystals larger than ∼1 μm favor spiral growth over 2D nucleation, irrespective of the supersaturation. Spirals prevail beyond this length scale because slow boundary layer diffusion creates a low surface supersaturation that favors the spiral mechanism. Sub-micron crystals favor 2D nucleation at high supersaturations, although diffusion can still limit the growth of nanoscopic crystals. Additives can change the dominant mechanism by impeding spiral growth or by directly promoting 2D nucleation

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm

We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/

Hydroxyl-rich macromolecules enable the bio-inspired synthesis of single crystal nanocomposites

Acidic macromolecules are traditionally considered key to calcium carbonate biomineralisation and have long been first choice in the bio-inspired synthesis of crystalline materials. Here, we challenge this view and demonstrate that low-charge macromolecules can vastly outperform their acidic counterparts in the synthesis of nanocomposites. Using gold nanoparticles functionalised with low charge, hydroxyl-rich proteins and homopolymers as growth additives, we show that extremely high concentrations of nanoparticles can be incorporated within calcite single crystals, while maintaining the continuity of the lattice and the original rhombohedral morphologies of the crystals. The nanoparticles are perfectly dispersed within the host crystal and at high concentrations are so closely apposed that they exhibit plasmon coupling and induce an unexpected contraction of the crystal lattice. The versatility of this strategy is then demonstrated by extension to alternative host crystals. This simple and scalable occlusion approach opens the door to a novel class of single crystal nanocomposites

Bayesian hierarchical clustering for microarray time series data with replicates and outlier measurements

Background Post-genomic molecular biology has resulted in an explosion of data, providing measurements for large numbers of genes, proteins and metabolites. Time series experiments have become increasingly common, necessitating the development of novel analysis tools that capture the resulting data structure. Outlier measurements at one or more time points present a significant challenge, while potentially valuable replicate information is often ignored by existing techniques. Results We present a generative model-based Bayesian hierarchical clustering algorithm for microarray time series that employs Gaussian process regression to capture the structure of the data. By using a mixture model likelihood, our method permits a small proportion of the data to be modelled as outlier measurements, and adopts an empirical Bayes approach which uses replicate observations to inform a prior distribution of the noise variance. The method automatically learns the optimum number of clusters and can incorporate non-uniformly sampled time points. Using a wide variety of experimental data sets, we show that our algorithm consistently yields higher quality and more biologically meaningful clusters than current state-of-the-art methodologies. We highlight the importance of modelling outlier values by demonstrating that noisy genes can be grouped with other genes of similar biological function. We demonstrate the importance of including replicate information, which we find enables the discrimination of additional distinct expression profiles. Conclusions By incorporating outlier measurements and replicate values, this clustering algorithm for time series microarray data provides a step towards a better treatment of the noise inherent in measurements from high-throughput genomic technologies. Timeseries BHC is available as part of the R package 'BHC' (version 1.5), which is available for download from Bioconductor (version 2.9 and above) via http://www.bioconductor.org/packages/release/bioc/html/BHC.html?pagewanted=all