Deep Learning Enhanced Density Functional Theory (DFT) based electronic structure simulation for Millions of Atoms (DeepDFT). We are developing a deep learning platform that can learn from millions of previously performed quantum mechanical DFT simulations to replace state of the art - an expensive iterative process, with linear scaling and parallelizable method which will make large-scale electronic structure simulations feasible. Training with a dataset of trillions of data points with very large feature vectors. Defining new fingerprint operators and functional architecture. Testing models with hundreds of millions of parameters.
|Effective start/end date||01/11/2018 → 31/08/2021|