Support for the UKCP consortium

Lead Research Organisation: University of Cambridge
Department Name: Materials Science & Metallurgy

Abstract

Many technological advances in modern day life are dependent upon the development of new materials, or better control and understanding of existing materials. Understanding the detailed properties of materials has therefore never been more important. The development of high quality computer simulation techniques has played an increasingly significant role in this endeavour over recent years. The UK has been at the forefront of this new wave, and the UKCP consortium has played an important part, in both developing computer codes and algorithms, and exploiting these new advances to increase our understanding of many industrially relevant materials and processes.

The preferred mechanism for providing computational resources on the UK national supercomputer (ARCHER) is via large research consortia, and this proposal funds the UKCP consortium. This is a large and established consortium, containing 22 different nodes and over 160 active researchers. Each node is a different University Department and is represented by one key academic - see the "Linked Proposals" or the Track Record for a complete list of current members of UKCP. This proposal seeks computational support for a large body of research (see "Other Support") with a substantial allocation of ARCHER resources and also the support of a named Research Software Engineer (RSE). The RSE will assist with training and supporting different members of the consortium in using the principle codes used within the consortium (e.g. CASTEP), and also develop some of the new code features required to complete some of these projects.

As part of this proposal, the researchers will have to develop new algorithms and also make theoretical improvements that will increase our simulation abilities (either by increasing the accuracy and reliability of calculations, or by enabling us to simulate bigger systems for longer). New algorithms include machine learning to generate new model potentials derived from accurate quantum mechanical calculations for fast calculations of large systems, improved structure optimisation, and uncertainty quantification. New functionality includes new spectroscopies, including magnetic structure, vibrations, neutron scattering and muon decay. Together, these innovations will enable the next generation of simulations and further widen our computational horizons.

The research described in this proposal will make significant impacts on many areas of future technology, such as semiconductor nanostructures, protein-drug optimization, ultra-high temperature ceramics, nanoscale devices, hybrid perovskites and solar cells and inorganic nanotubes and metal-air battery anodes.

There are also areas of fundamental research, designed to push our understanding of basic properties of matter, such as interfacial water, nanocrystal growth, structure of grain boundaries, pigment-protein complexes, radiation damage in DNA and high-pressure hydrogen phases.

The research proposed does not easily fit into any of the traditional categories of 'physics' or 'chemistry' etc. Instead, the UKCP is a multi-disciplinary consortium using a common theoretical foundation to advance many different areas of materials-based science which has the potential for significant impact both in the short and long-term.
 
Title Data for "Stochastic sampling of quadrature grids for the evaluation of vibrational expectation values" 
Description Data for "Stochastic sampling of quadrature grids for the evaluation of vibrational expectation values" 
Type Of Material Database/Collection of data 
Year Produced 2018 
Provided To Others? Yes  
 
Title Data for "Stochastic sampling of quadrature grids for the evaluation of vibrational expectation values" 
Description Data for "Stochastic sampling of quadrature grids for the evaluation of vibrational expectation values" 
Type Of Material Database/Collection of data 
Year Produced 2018 
Provided To Others? Yes  
 
Title Finite field formalism for bulk electrolyte solutions (data set) 
Description All simulations can be run with the LAMMPS code (22 Sept 2017). See also https://github.com/uccasco/FiniteFields for additional source code required to apply the constant D ensemble. Constant E simulations can be performed with in.SPCE.efield. The user will need to replace with the appropriate value of E (along the z direction). Constant D simulations can be performed with in.SPCE.dfield. Likewise, the user will need to replace with the appropriate value of D. Also included are configurations corresponding to different concentrations (these have been obtained from D = 0 simulations). The number of ions pairs and water molecules is given by the filename e.g. 10pair_256wat.data contains 10 ion pairs and 256 water molecules. The name of the configuration file will need to replace the text "" on line 13 of the input files. Note the user may wish to perform shorter simulations for equilibration purposes, especially when changing the value of Dz, or switching to the E ensemble. See included README file. 
Type Of Material Database/Collection of data 
Year Produced 2019 
Provided To Others? Yes  
 
Title Research data supporting "A predictive modelling study of the impact of chemical doping on the strength of a Ag/ZnO interface" 
Description The data and lattice structures used to calculate the interfacial adhesion and bond populations are available in this dataset. 
Type Of Material Database/Collection of data 
Year Produced 2018 
Provided To Others? Yes  
 
Title Research data supporting "Controlling Ag diffusion in ZnO by donor doping: a first principles study" 
Description The data and lattice structures used to calculate the formation energies and diffusion barriers are available in this dataset. The transition state structures used for charge density analysis are also provided. 
Type Of Material Database/Collection of data 
Year Produced 2017 
Provided To Others? Yes  
 
Title Research data supporting "Controlling Ag diffusion in ZnO by donor doping: a first principles study" 
Description The data and lattice structures used to calculate the formation energies and diffusion barriers are available in this dataset. The transition state structures used for charge density analysis are also provided. 
Type Of Material Database/Collection of data 
Year Produced 2017 
Provided To Others? Yes  
 
Title Research data supporting "Investigating Sodium Storage Mechanisms in Tin Anodes: A Combined Pair Distribution Function Analysis, Density Functional Theory, and Solid-State NMR Approach" 
Description Raw and processed PDF, XRD, electrochemistry, ssNMR data and CIF files along with corresponding metadata for all measurements published in the paper "Investigating Sodium Storage Mechanisms in Tin Anodes: A Combined Pair Distribution Function Analysis, Density Functional Theory and Solid-State NMR Approach." Specifically, we provide PDF data as .hdf5 or .tif (raw unprocessed) and .csv (integrated and extracted) files, XRD data as .tif (raw unprocessed) and .csv (integrated and extracted) files, electrochemistry data as .csv (plain text) files, and unprocessed NMR data in the IUPAC standard JCAMP-DX format, processed data available as .csv. We refer the reader to the aforementioned paper for further details. 
Type Of Material Database/Collection of data 
Year Produced 2017 
Provided To Others? Yes  
 
Title Research data supporting "Investigating Sodium Storage Mechanisms in Tin Anodes: A Combined Pair Distribution Function Analysis, Density Functional Theory, and Solid-State NMR Approach" 
Description Raw and processed PDF, XRD, electrochemistry, ssNMR data and CIF files along with corresponding metadata for all measurements published in the paper "Investigating Sodium Storage Mechanisms in Tin Anodes: A Combined Pair Distribution Function Analysis, Density Functional Theory and Solid-State NMR Approach." Specifically, we provide PDF data as .hdf5 or .tif (raw unprocessed) and .csv (integrated and extracted) files, XRD data as .tif (raw unprocessed) and .csv (integrated and extracted) files, electrochemistry data as .csv (plain text) files, and unprocessed NMR data in the IUPAC standard JCAMP-DX format, processed data available as .csv. We refer the reader to the aforementioned paper for further details. 
Type Of Material Database/Collection of data 
Year Produced 2017 
Provided To Others? Yes  
 
Title Stabilization of AgI's polar surfaces by the aqueous environment, and its implications for ice formation (data set) 
Description See also the README file. This dataset contains three sub-directories: (1) D0o0 -- contains input files for performing simulations at D = 0 (2) DCNC -- contains input files for performing simulations at DCNC (2) ECNC -- contains input files for performing simulations at ECNC All simulations can be run with the LAMMPS code (16 Mar 2018). See also https://github.com/uccasco/FiniteFields for additional source code required to apply the E and D fields. Each directory contains the followings files: (a) init.data -- initial structure for pure water in contact with AgI. (b) in.tip4p2005.equil -- input file for performing the initial equilibration of the system at 252K. (c) in.tip4p2005.cool -- input file for performing the cooling ramp simulation between 252K and 242K. (d) in.tip4p2005.constT -- input file for performing a constant T simulation at 242K. The above files perform simulations with an immobile AgI crystal. The DCNC additionally contains a file "in.tip4p2005.constT.mob" which demonstrates the changes needed to perform a simulation with a mobile AgI crystal. (The other equilibration and cooling input files can be similarly adapted.) The ECNC and DCNC directories also contain a file "init.data.electrolyte" which contains an initial structure for NaCl electrolyte in contact with AgI. Please see Table S1 of the article for values of D and E fields used. AgI.table -- tabulated interatomic potential for AgI crystal. 
Type Of Material Database/Collection of data 
Year Produced 2019 
Provided To Others? Yes  
 
Title AIRSS 
Description Ab initio Random Structure Searching (AIRSS) is a very simple, yet powerful and highly parallel, approach to structure prediction. The concept was introduced in 2006 and its philosophy more extensively discussed in 2011. Random structures - or more precisely, random "sensible" structures - are generated and then relaxed to nearby local energy minima. Particular success has been found using density functional theory (DFT) for the energies, hence the focus on "ab initio" random structure searching. The sensible random structures are constructed so that they have reasonable densities, and atomic separations. Additionally they may embody crystallographic, chemical or prior experimental/computational knowledge. Beyond these explicit constraints the emphasis is on a broad, uniform, sampling of structure space. AIRSS has been used in a number of landmark studies in structure prediction, from the structure of SiH4 under pressure to providing the theoretical structures which are used to understand dense hydrogen (and anticipating the mixed Phase IV), incommensurate phases in aluminium under terapascal pressures, and ionic phases of ammonia. The approach naturally extends to the prediction clusters/molecules, defects in solids, interfaces and surfaces (interfaces with vacuum). The AIRSS package is tightly integrated with the CASTEP first principles total energy code. However, it is relatively straightforward to modify the scripts to use alternative codes to obtain the core functionality, and examples are provided. The AIRSS package is released under the GPL2 licence. 
Type Of Technology Software 
Year Produced 2017 
Impact It appears that researcher are routinely using AIRSS. 
URL https://www.mtg.msm.cam.ac.uk/Codes/AIRSS