2017) 'Predicting crystal growth via a uni ed kinetic three-dimensional partition model. ', Nature., 544 (7651). pp. 456-459. Further information on publisher's website:https://doi.org/10.1038/nature21684Publisher's copyright statement:Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Understanding and predicting the course of crystal growth is fundamental to the control of functionality in modern materials. Despite investigations for over one hundred years 1-5 it is only recently that the molecular intricacies of these processes have been revealed by scanning probe microscopies 6-8 . In order to bring some order and understanding to this vast amount of new information requires new rules to be developed and tested. To date, because of the complexity and variety of different crystal systems, this has relied on developing models that are usually constrained to one system only 9-11 . Such work is painstakingly slow and will not be able to achieve the wide scope of understanding in order to create a unified model across crystal types and crystal structures. Here we describe a new approach to understand and, in theory, predict the growth of crystals, including the incorporation of defect structures, by simultaneous molecular-scale simulation of crystal habit and surface topology using a unified kinetic 3-D partition model. We exemplify our approach by predicting the crystal growth of a diverse set of crystal types including zeolites, metal-organic frameworks, calcite, urea and L-cystine.By understanding crystal growth at the molecular scale we have the possibility to control crystal habit, crystal size, the elimination or incorporation of defects and the development of intergrowth structures. As crystals are used in technologies from pharmaceuticals to gas storage and separation materials, from optoelectronic devices to heterogeneous catalysts, such understanding is vital. If we take an example of a very complex and yet very important crystal type, that of zeolites 12 which form the backbone of the heterogeneous catalysis industry, then many of the problems that must be addressed in crystal growth can be illustrated. Zeolites are nanoporous materials were the framework of the material is constructed from a strong covalently bonded network of Si -O and Al -O bonds. The pores of the material are filled with water and cations that balance the negative charge on the framework. Crystals of zeolites grow from aqueous solutions at temperatures up to about 230 o C and it is well known from NMR spectroscopy that...

Generic in silico methodology – CrystalGrower – for simulating crystal habit and nanoscopic surface topology to determine crystallisation free energies.

No abstract

We present Longbow, a lightweight console-based remote job submission tool and library. Longbow allows the user to quickly and simply run jobs on high performance computing facilities without leaving their familiar desktop environment. Not only does Longbow greatly simplify the management of computeintensive jobs for experienced researchers, it also lowers the technical barriers surrounding high performance computation for the next generation of scientists and engineers. Longbow has already been used to remotely submit jobs in a number of projects and has the potential to redefine the manner in which high performance computers are used.

The configuration of most current academic high-performance computing (HPC) resources tends to enforce ways of working with, and thinking about, molecular dynamics (MD) simulations that are not always optimal. For example, when the aim of the simulation(s) is to produce a representative sample of a Boltzmann weighted ensemble, the ideal scenario would be to be able to do just that -i.e. to tap into a running simulation of indefinite length, collect data from it in real time, and only terminate the simulation once the quality of a sample was assured. Current approaches, based on batch jobs of proscribed maximum length, and a post-processing style of data analysis, inhibit this. In the spirit of the Internet of Things, we have developed Tios, a Python application that turns MD simulations into remotely discoverable and accessible streaming web applications to which researchers can connect and download data as they please. Tios is freely available, works with standard MD codes, and requires no modifications to them. In this paper we outline how Tios works and present a number of test cases that demonstrate its capabilities.