no code implementations • 7 Dec 2023 • Peter Bjørn Jørgensen, Jonas Busk, Ole Winther, Mikkel N. Schmidt
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.
no code implementations • 10 May 2023 • Jonas Busk, Mikkel N. Schmidt, Ole Winther, Tejs Vegge, Peter Bjørn Jørgensen
The proposed method considers both epistemic and aleatoric uncertainty and the total uncertainties are recalibrated post hoc using a nonlinear scaling function to achieve good calibration on previously unseen data, without loss of predictive accuracy.
no code implementations • 20 Jul 2022 • Mathias Schreiner, Arghya Bhowmik, Tejs Vegge, Peter Bjørn Jørgensen, Ole Winther
We also compare with and outperform Density Functional based Tight Binding (DFTB) on both accuracy and computational resource.
1 code implementation • 1 Dec 2021 • Peter Bjørn Jørgensen, Arghya Bhowmik
Electron density $\rho(\vec{r})$ is the fundamental variable in the calculation of ground state energy with density functional theory (DFT).
no code implementations • 13 Jul 2021 • Jonas Busk, Peter Bjørn Jørgensen, Arghya Bhowmik, Mikkel N. Schmidt, Ole Winther, Tejs Vegge
In this work we extend a message passing neural network designed specifically for predicting properties of molecules and materials with a calibrated probabilistic predictive distribution.
1 code implementation • 4 Nov 2020 • 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 ground state properties can be calculated.
2 code implementations • 15 May 2019 • Peter Bjørn Jørgensen, Estefanía Garijo del Río, Mikkel N. Schmidt, Karsten Wedel Jacobsen
The possibilities for prediction in a realistic computational screening setting is investigated on a dataset of 5976 ABSe$_3$ selenides with very limited overlap with the OQMD training set.
5 code implementations • 8 Jun 2018 • 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.
Ranked #3 on Formation Energy on Materials Project