Machine learning for solar energy is supercomputer kryptonite
Supercomputers could find themselves out of a job thanks to a suite of new machine learning models that produce rapid, accurate results using a normal laptop.
Researchers at the ARC Centre of Excellence in Exciton Science, based at RMIT University, have written a program that predicts the band gap of materials, including for solar energy applications, via freely available and easy-to-use software. Band gap is a crucial indication of how efficient a material will be when designing new solar cells.
Band gap predictions involve quantum and atomic-scale chemical calculations ...






