13 citations · 13 across the 3 of their papers we have counts for
4 papers
Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networks
Jonas Busk, Peter Bjørn Jørgensen, Arghya Bhowmik +3
Data-driven methods based on machine learning have the potential to accelerate computational analysis of atomic structures. In this context, reliable uncertainty estimates are impo…
Data Management Plans: the Importance of Data Management in the BIG-MAP Project
Ivano E. Castelli, Daniel J. Arismendi-Arrieta, Arghya Bhowmik +27
Open access to research data is increasingly important for accelerating research. Grant authorities therefore request detailed plans for how data is managed in the projects they fi…
Automatic diffusion path exploration for multivalent battery cathodes using geometrical descriptors
Felix T. Bölle, Arghya Bhowmik, Tejs Vegge +2
Stable and fast ionic conductors for magnesium cathode materials have the prospect of enabling high energy density batteries beyond current Lithium-ion technologies. So far, only a…
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…