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Data-driven models in molecular dynamics

This project is no longer listed in the FindAPhD
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  • Full or part time
    Prof B Leimkuhler
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Multiscale modelling plays an essential role in molecular simulation as the range of scales involved precludes the use of a single, unified system of equations. The most accurate model is quantum mechanics which describes the evolution of a system of nuclei and electrons. When a modest-sized quantum system is discretized for numerical solution, there results an unimaginably large number of equations which can swamp even the most powerful computer systems. A classical model based on potential energy functions for the interaction of atomic nuclei provides a much simplified description, but one that precludes many important effects (breakage of bonds, quantum tunnelling, etc.). Even the classical description must be further ’coarse-grained’ to provide an effective scheme for large scale or slow-developing processes that would otherwise remain inaccessible in computer simulation. In a multiscale model, different models are unified by the use of bridging algorithms, numerical and analytical averaging, and reliance on the principles of statistical mechanics. In this project, the goal is to use experimental data in place of simulation data to capture complex local processes and low-level interactions in a molecular system . A system is no longer viewed as being described by a single inter-molecular potential energy surface, but rather by a collection of surfaces which can be locally determined, on-the-fly, from tabulated data. The resulting procedures will engender methodological changes in order to retain statistical properties that are relevant for the simulator. This project has aspects of molecular dynamics, computational statistical mechanics and quantum mechanics. It further relates to machine learning and has applications in materials modelling. Informal enquiries can be made to Ben Leimkuhler ([email protected]).

Funding Notes

Funding is available through competitive scholarships; http://www.maths.ed.ac.uk/school-of-mathematics/studying-here/pgr/funding-opportunities for details. To be considered for these, applicants need to meet the following deadline: 1st December 2017 for early admission, and 31 January 2018 for standard admission.

How good is research at University of Edinburgh in Mathematical Sciences?
(joint submission with Heriot-Watt University)

FTE Category A staff submitted: 56.80

Research output data provided by the Research Excellence Framework (REF)

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