13 citations · 13 across the 1 of their papers we have counts for
3 papers
DeepDFT: Neural Message Passing Network for Accurate Charge Density Prediction
Peter Bjørn Jørgensen, Arghya Bhowmik
We introduce DeepDFT, a deep learning model for predicting the electronic charge density around atoms, the fundamental variable in electronic structure simulations from which all g…
Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors
Peter Bjørn Jørgensen, Estefanía Garijo del Río, Mikkel N. Schmidt +1
Computational materials screening studies require fast calculation of the properties of thousands of materials. The calculations are often performed with Density Functional Theory…
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
Peter Bjørn Jørgensen, Karsten Wedel Jacobsen, Mikkel N. Schmidt
Neural message passing on molecular graphs is one of the most promising methods for predicting formation energy and other properties of molecules and materials. In this work we ext…