MDAnalysis (http://mdanalysis.org) is a library for structural and temporal analysis of molecular dynamics (MD) simulation trajectories and individual protein structures. MD simulations of biological molecules have become an important tool to elucidate the relationship between molecular structure and physiological function. Simulations are performed with highly optimized software packages on HPC resources but most codes generate output trajectories in their own formats so that the development of new trajectory analysis algorithms is confined to specific user communities and widespread adoption and further development is delayed. MDAnalysis addresses this problem by abstracting access to the raw simulation data and presenting a uniform object-oriented Python interface to the user. It thus enables users to rapidly write code that is portable and immediately usable in virtually all biomolecular simulation communities. The user interface and modular design work equally well in complex scripted work flows, as foundations for other packages, and for interactive and rapid prototyping work in IPython / Jupyter notebooks, especially together with molecular visualization provided by nglview and time series analysis with pandas. MDAnalysis is written in Python and Cython and uses NumPy arrays for easy interoperability with the wider scientific Python ecosystem. It is widely used and forms the foundation for more specialized biomolecular simulation tools. MDAnalysis is available under the GNU General Public License v2.

We present a simple multiscale model for polymer chains in which it is possible to selectively remove degrees of freedom. The model integrates all-atom and coarse-grained potentials in a simple and systematic way and allows a fast sampling of the complex conformational energy surface typical of polymers whilst maintaining a realistic description of selected atomistic interactions. In particular, we show that it is possible to simultaneously reproduce the structure of highly directional non-bonded interactions such as hydrogen bonds and efficiently explore the large number of conformations accessible to the polymer chain. We apply the method to a melt of polyamide removing from the model only the degrees of freedom associated to the aliphatic segments and keeping at atomistic resolution the amide groups involved in the formation of the hydrogen bonds. The results show that the multiscale model produces structural properties that are comparable with the fully atomistic model despite being five times faster to simulate.

The development of rational crystallisation strategies in polymorphic systems requires the experimental manipulation of both kinetics and thermodynamics. We show for the first time the results of the interplay between these competing driving forces in an enantiotropic system, p-aminobenzoic acid. The outcomes are unexpected with temperature having no impact.The enormous scientific and commercial significance of crystalline polymorphism underlines the importance of rational crystallisation strategies for preparing structurally pure polymorphic materials from solution. In 2000 Threlfall noted 1 that 'Most of the accounts which purport to address this issue prove on close examination to be plausible deductions from a limited set of specific experimental observations, but unrelatable to the general problem of the interaction between thermodynamic and kinetic factors…'. In fact our best guide in this context remains Ostwald's rule of stages first put forward in 1897. 2 For the many systems in which polymorphic forms are monotropically related much recent work (e.g. glycine, 3 L-glutamic acid, 4 2,6 dihydroxybenzoic acid, 5 o and m-aminobenzoic acids, 6,7 mannitol, 8 benzamide 9 ) offers continued support of the rule with initial crystallisation of metastable phases over a wide range of conditions. Limited data on the water/inosine system 10 showed this to be the case for the crystallisation of the two monotropically related polymorphs but below 10°C, where the enantiotropically related dihydrate exists, direct crystallisation of this stable phase was possible. Apart from this and the much earlier report of Sato and Boistelle 11 on polymorphic mixtures of stearic acid, we are unaware of any systematic studies concerning the relationship between polymorph appearance, solvent, supersaturation and temperature in an enantiotropic system. It is our belief, surprisingly that this current work is the first to explore this topic for a molecular material crystallising from solution.As a model system we have chosen p-aminobenzoic acid (PABA) crystallising from aqueous and ethanolic solutions. The two enantiotropically related polymorphs, α and β have a transition temperature 13.8°C 12 with known crystal structures. 13,14 α is the stable form above 13.8°C and β below. The two forms are based on differing hydrogen bonded motifs and have distinct needle and rhombic morphologies. 15 Gracin and Rasmuson 16 reported that in aqueous solutions both forms, α and β, can be crystallised, while only α PABA appears from organic solvents, over a wide temperature range. Svard et al. 17 confirmed this in 330 cooling crystallisation experiments in the temperature range 15-30°C from methanol, acetonitrile and ethyl acetate. Most recently Sullivan et al. 18 used the induction time probability technique to determine the nucleation rates of α PABA from 2-propanol, acetonitrile and ethyl acetate and demonstrated the importance of desolvation and self-assembly in the nucleation pathway. Again, in these experiments performed at 20°C, over the supersatu...

In this work we set out to evaluate the computational performance of several popular Monte Carlo simulation programs, namely Cassandra, DL Monte, Music, Raspa and Towhee, in modelling gas adsorption in crystalline materials. We focus on the reference case of CO 2 adsorption in IRMOF-1 at 208 K. To critically assess their performance, we first establish some criteria which allow us to make this assessment on a consistent basis. Specifically, the total computational time required for a program to complete a simulation of an adsorption point, consists of the time required for equilibration plus time required to generate a specific number of uncorrelated samples of the property of interest. Our analysis shows that across different programs there is a wide difference in the statistical value of a single MC step, however their computational performance is quite comparable. We further explore the use of energy grids and energy bias techniques, as well as the efficiency of the parallel execution of the simulations. The test cases developed are made openly available as a resource for the community, and can be used for validation and as a template for further studies.

In hybrid particle models where coarse-grained beads and atoms are used simultaneously, two clearly separate time scales are mixed. If such models are used in molecular dynamics simulations, a multiple time step (MTS) scheme can therefore be used. In this manuscript, we propose a simple MTS algorithm which approximates for a specific number of integration steps the slow coarse-grained bead-bead interactions with a Taylor series approximation while the atom-atom ones are integrated every time step. The procedure is applied to a previously developed hybrid model of a melt of atactic polystyrene (di Pasquale, Marchisio, and Carbone, J. Chem. Phys. 2012, 137, 164111). The results show that structure, local dynamics, and free diffusion of the model are not altered by the application of the integration scheme which can confidently be used to simulate multiresolved models of polymer melts.

In science the filesystem often serves as a de facto database, with directory trees being the zeroth-order scientific data structure. But it can be tedious and error prone to work directly with the filesystem to retrieve and store heterogeneous datasets. datreant makes working with directory structures and files Pythonic with Treants: specially marked directories with distinguishing characteristics that can be discovered, queried, and filtered. Treants can be manipulated individually and in aggregate, with mechanisms for granular access to the directories and files in their trees. Disparate datasets stored in any format (CSV, HDF5, NetCDF, Feather, etc.) scattered throughout a filesystem can thus be manipulated as meta-datasets of Treants. datreant is modular and extensible by design to allow specialized applications to be built on top of it, with MDSynthesis as an example for working with molecular dynamics simulation data.

MDAnalysis is an object-oriented Python library to analyze trajectories from molecular dynamics (MD) simulations in many popular formats. With the development of highly optimized MD software packages on high performance computing (HPC) resources, the size of simulation trajectories is growing up to many terabytes in size. However efficient usage of multicore architecture is a challenge for MDAnalysis, which does not yet provide a standard interface for parallel analysis. To address the challenge, we developed PMDA, a Python library that builds upon MDAnalysis to provide parallel analysis algorithms. PMDA parallelizes common analysis algorithms in MDAnalysis through a task-based approach with the Dask library. We implement a simple split-apply-combine scheme for parallel trajectory analysis. The trajectory is split into blocks, analysis is performed separately and in parallel on each block ("apply"), then results from each block are gathered and combined. PMDA allows one to perform parallel trajectory analysis with pre-defined analysis tasks. In addition, it provides a common interface that makes it easy to create user-defined parallel analysis modules. PMDA supports all schedulers in Dask, and one can run analysis in a distributed fashion on HPC machines, ad-hoc clusters, a single multi-core workstation or a laptop. We tested the performance of PMDA on single node and multiple nodes on a national supercomputer. The results show that parallelization improves the performance of trajectory analysis and, depending on the analysis task, can cut down time to solution from hours to minutes. Although still in alpha stage, it is already used on resources ranging from multi-core laptops to XSEDE supercomputers to speed up analysis of molecular dynamics trajectories. PMDA is available as open source under the GNU General Public License, version 2 and can be easily installed via the pip and conda package managers.

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