73 citations · 181 across the 8 of their papers we have counts for
8 papers
Coherent energy and force uncertainty in deep learning force fields
Peter Bjørn Jørgensen, Jonas Busk, Ole Winther +1
In machine learning energy potentials for atomic systems, forces are commonly obtained as the negative derivative of the energy function with respect to atomic positions. To quanti…
Danish Foundation Models
Kenneth Enevoldsen, Lasse Hansen, Dan S. Nielsen +10
Large language models, sometimes referred to as foundation models, have transformed multiple fields of research. However, smaller languages risk falling behind due to high training…
Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces
Jonas Busk, Mikkel N. Schmidt, Ole Winther +2
Inexpensive machine learning potentials are increasingly being used to speed up structural optimization and molecular dynamics simulations of materials by iteratively predicting an…
NeuralNEB -- Neural Networks can find Reaction Paths Fast
Mathias Schreiner, Arghya Bhowmik, Tejs Vegge +2
Quantum mechanical methods like Density Functional Theory (DFT) are used with great success alongside efficient search algorithms for studying kinetics of reactive systems. However…
Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
Peter Bjørn Jørgensen, Arghya Bhowmik
Electron density is the fundamental variable in the calculation of ground state energy with density functional theory (DFT). Beyond total energy, features and changes…
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…