2D material band-gap prediction by machine learning

Two-dimensional (2D) semiconductor materials are materials with thickness on the atomic scale that provide unique properties compared to their 3D counterparts. One important property of semiconductors is their band gap, which dictates how the semiconductor material will behave. However, manufacturing and testing 2D semiconductors can be costly and difficult, so the ability to predict the band gap of 2D semiconductors would be very useful to allow researchers to focus efforts on testing materials that will yield desired band gaps. This project aims to train a machine learning algorithm on data from 2D and 3D semiconductors to predict the band gap of new 2D semiconductor materials. This machine learning approach has the potential to be more accurate and quicker to compute than current quantum-mechanics based computations. This project will benefit both institutions by developing the application of machine learning in materials engineering, as well as providing an algorithm that can be used by researchers and engineers to design new 2D semiconductor materials.

Faculty Supervisor:

Arthur Chan

Student:

Partner:

National University of Singapore

Discipline:

Engineering

Sector:

Education

University:

University of Toronto

Program: