The Knowledge Led Master Code

UCL Online >> Chemistry >> MC@KLB >> KLMC
Webmaster: Dr Scott Woodley

Welcome to the homepage for our latest software - KLMC.

KLMC, or more specifically the Knowledge Led Master Code, was created with the desire to: automate many tasks traditionally performed by the user of a range of third party codes (listed below in the right hand column); enable a multistage approach where the KLMC code learns on the fly and refines input files that are submitted for new calculations - hence the name knowledge led; and be able to exploit massively parallel computer platforms for a more general set of applications that may require statistical sampling. The KLMC code is witten in Fortran90 and uses MPI to simultaneously exploit more than one processor.

  KLMC is capable of:

Updating a Simple Database of Structures (or Solutions);
Rank and Rename Structures;
Task Farming (Structures from Database);
Perform Global Optimisation;
Adapt Interatomic Potentials;
Add Images (Metallic Surfaces);
Performing Post Analyses:
Compute Radial Distribution Functions,
Ensemble Average - Boltzmann Weighted - Properties

KLMC can link up with -


- by generating appropriate input files;
reading/extracting data from outputs file;
and can run these on the same platform (either through system calls or as library file) or remotely (KLMC runs itself on a local machine and spawns many individual calculations, or tasks, on larger resources elsewhere).

Applications include: (a) Simple task farming (screening structures from the database through a third party code); (b) Structure prediction of nano-sized clusters in vacuum or on a surface; structure prediction of bulk phases; and structure prediction of surface reconstructions - using a range of global optimisation techniques based on basin hopping and genetic algorithms; And (c) statistical sampling of solid solutions or multiple point defects in a crystalline solid.

Publication of work using KLMC:

Knowledge Led Master Code Search for Atomic and Electronic Structures of LaF3 Nanoclusters on Hybrid Rigid Ion - Shell Model - DFT Landscapes, Scott M. Woodley, J. Phys. Chem. C, 2013, 117 (45), pp 24003-24014, DOI: 10.1021/jp406854j

Structure Prediction of Nanoclusters; a Direct or a Pre-screened Search on the DFT Energy Landscape? Matthew R. Farrow, Yee Chow and Scott M. Woodley, Phys. Chem. Chem. Phys., 2014, 16 (39), pp 21119-21134, DOI: 10.1039/c4cp01825g

Interlayer Cation Exchange Stabilizes Polar Perovskite Surfaces, Daniel E. E. Deacon-Smith, David O. Scanlon, C. Richard A. Catlow, Alexey A. Sokol and Scott M. Woodley, Adv. Mater., 2014, 26 (42), pp 7252-7256, DOI:10.1002/adma.201401858

From monomer to monolayer: a global optimisation study of (ZnO)n nanoclusters on the Ag surface, Ilker Demiroglu, Scott M. Woodley, Alexey A. Sokol, Stefan T. Bromley, Nanaoscale, 2014, 6 (24), pp 14754-14765, DOI: 10.1063/1.4820415

We are grateful for EPSRC support: (EP/I03014X) over two years during which we were able to develop the parallel capabilities of KLMC; (EP/F067496) over five years during which we gained access to HECToR - UK's national high performance computer facilities - via our membership of the Materials Chemistry Consortium. We also acknowledge the use of local HPC facilities - namely, Legion - which is provided by UCL.

Other Publications Acknowledging EP/I03014X:

Embedded-cluster calculations in a numeric atomic orbital density-functional theory framework, Daniel Berger, Andrew J. Logsdail, Harald Oberhofer, Matthew R. Farrow, C. Richard A. Catlow, Paul Sherwood, Alexey A. Sokol, Volker Blum and Karsten Reuter, J. Chem. Phys., 2014, 141, 024105, DOI:10.1063/1.4885816

Applying a new interatomic potential for the modelling of hexagonal and orthorhombic YMnO3, Ning Jiang, Scott M. Woodley, C. Richard A. Catlow, X. Zhang, J. Mater. Chem. C, 2015, 3 (18), pp 4787-4793, DOI: 10.1039/c4tc02759k