EPSRC Reference: |
EP/C015231/1 |
Title: |
Polarization for molecular simulation from a neural network trained by ab initio electron densities of clusters |
Principal Investigator: |
Popelier, Professor P |
Other Investigators: |
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Researcher Co-Investigators: |
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Project Partners: |
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Department: |
Chemistry |
Organisation: |
University of Manchester, The |
Scheme: |
Standard Research (Pre-FEC) |
Starts: |
01 October 2005 |
Ends: |
30 September 2008 |
Value (£): |
94,556
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EPSRC Research Topic Classifications: |
Chemical Biology |
Gas & Solution Phase Reactions |
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EPSRC Industrial Sector Classifications: |
Chemicals |
Pharmaceuticals and Biotechnology |
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Related Grants: |
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Panel History: |
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Summary on Grant Application Form |
We want to enhance the realism of potentials for Molecular Simulation, with liquid water as a main priority. In particular we propose a radically different way of treating polarization, expected to create most improvement at short-range. For that purpose we retrieve very detailed information from ab initio electron densities of small water clusters. We use the rigorous partitioning technique of Quantum Chemical Topology(QCT) to isolate a single water molecule from its condensed matter environment. We record this molecule's atomic multipole moments in direct response to an ever fluctuating environment, generated by a MD simulation that lacks polarization. Given the complexity of this response we invoke artificial intelligence to predict atomic multipole moments of a central water molecule, in a water environment hitherto not encountered. Preliminary work on HF clusters shows that the basic idea works and that training is delivering excellent predictions. A new MD simulation is then performed with the multipole-moment-response predictor, bringing about only a small computational overhead to the electrostatic multipole-multipole interaction part, which has been consistently extended into the Ewald summation. We expect this realistic representation of polarisation to deliver better agreement with experiment for a dozen bulk properties. The successful model will be explored in a later, more ambitious stage to solvation of amino acids and DNA base pairs.
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Key Findings |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
Summary |
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Date Materialised |
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Sectors submitted by the Researcher |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Project URL: |
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Further Information: |
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Organisation Website: |
http://www.man.ac.uk |