Enhancing Classical Simulations with Electronic Corrections and Artificial Neural Networks



This project is now assigned to a student.

Classical simulations of materials and nanoparticles have the advantage of speed and scalability, but lack the ability to describe the electronic properties.  Quantum mechanical and ab initio simulations have the advantage of providing accurate estimations of the electronic structure and charge transfer, but are typically limited to small and simple systems.  In this project identical sets of classical and electronic structure simulations will be used to train an artificial neural network to predict a correction term, and the Fermi energy of arbitrary gold nanoparticles.  This 6 credit point project involves some scientific programming to characterise the data and extract suitable features, data science and simple introductory machine learning.


coding in C++ and python, good mathematics and statistics, interest in machine learning


nanoparticles, materials science, simulation, scientific programming, computational science, machine learning, neural networks

Updated:  10 August 2021/Responsible Officer:  Head of School/Page Contact:  CECS Webmaster