Postdoc in creating molecular force fields for electrolyte compounds

University of Cambridge

First advertised here 26 June 2020

The task of the Cambridge group is to create molecular force fields for electrolyte compounds based on data supplied from Uppsala, and to compute some properties using molecular dynamics that are feed forward to the Vienna group and others within the larger BIGMAP project. The approach for force field fitting is rooted in machine learning ideas, initially using the Gaussian Approximation Potential (GAP) framework, but over the course of the project, new formulations based on polynomial regression will also be tried. Apart from the molecular force fields themselves, the electrolyte-electrode interfaces will also be considered, and the force fields extended to include the solid-molecule interactions, something that has been traditionally very difficult with conventional force fields and potentials, and thus the capabilities of the general machine learning force field framework will be exploited to their full extent. 

Special attention will be given to the intermolecular part of the force field, using a hierarchy of approaches:  multiscale descriptors, electrostatic baseline models, explicit machine learning of electrostatic and vdW parameters will all play a part. 

The ideal applicant will significant experience in molecular modelling. Experience in machine learning, particularly in the materials/chemistry space, would be useful, but not essential. Similarly, experience in fitting conventional molecular force fields would be very beneficial, but not essential.

Gábor Csányi, University of Cambridge


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