Imperial College London

Dr Laura Ratcliff

Faculty of EngineeringDepartment of Materials

EPSRC Early Career Research Fellow







2M14Royal School of MinesSouth Kensington Campus





I am an EPSRC Early Career Research Fellow in the Department of Materials.  My research centres around the development of new methods for first principles materials modelling, with a focus on simulating large systems containing several hundred to a few tens of thousands of atoms.  I'm a developer of three codes which use density functional theory, and am interested both in the development of software which enables the efficient use of massively parallel computers, and in the application of such software to a range of materials and properties.

For more information, please also visit my research group website.


  • 04/2017 - 11/2017: Research Associate, Department of Materials, Imperial College London
  • 03/2014 - 03/2017: Postdoctoral Appointee, Leadership Computing Facility, Argonne National Laboratory, Illinois, USA
  • 02/2012 - 02/2014: Postdoctoral Researcher, L_Sim Laboratory of Atomistic Simulation, CEA Grenoble, France
  • 10/2011 - 12/2011: Research Assistant, Department of Materials, Imperial College London
  • 10/2008 - 09/2011: PhD, Department of Materials, Imperial College London
  • 10/2004 - 06/2008: MPhys Theoretical Physics (with a year in Europe), University of York
  • 09/2006 - 05/2007: Erasmus exchange, L’Université des Sciences et Technologies de Lille, France



Dawson W, Mohr S, Ratcliff LE, et al., 2020, Complexity reduction in density functional theory calculations of large systems: system partitioning and fragment embedding., Journal of Chemical Theory and Computation, Vol:16, ISSN:1549-9618, Pages:2952-2964

Prentice J, Aarons J, Womack JC, et al., 2020, The ONETEP linear-scaling density functional theory program, The Journal of Chemical Physics, Vol:152, ISSN:0021-9606, Pages:174111-1-174111-36

Zaccaria M, Dawson W, Cristiglio V, et al., 2020, Designing a bioremediator: mechanistic models guide cellular and molecular specialization, Current Opinion in Biotechnology, Vol:62, ISSN:0958-1669, Pages:98-105

Pi JM, Stella M, Fernando NK, et al., 2020, Predicting core level photoelectron spectra of amino acids using density functional theory., Journal of Physical Chemistry Letters, Vol:11, ISSN:1948-7185, Pages:2256-2262

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