no code implementations • 25 Jul 2022 • Mathias Schreiner, Arghya Bhowmik, Tejs Vegge, Jonas Busk, Ole Winther
In this work, we present the dataset Transition1x containing 9. 6 million Density Functional Theory (DFT) calculations of forces and energies of molecular configurations on and around reaction pathways at the wB97x/6-31G(d) level of theory.
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.