5 papers
Programmatic Policy Extraction by Iterative Local Search
Rasmus Larsen, Mikkel Nørgaard Schmidt
Reinforcement learning policies are often represented by neural networks, but programmatic policies are preferred in some cases because they are more interpretable, amenable to for…
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…
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…
Probabilistic PARAFAC2
Philip J. H. Jørgensen, Søren F. V. Nielsen, Jesper L. Hinrich +3
The PARAFAC2 is a multimodal factor analysis model suitable for analyzing multi-way data when one of the modes has incomparable observation units, for example because of difference…
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…