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 • 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.
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 • 10 May 2020 • Christopher Rose, Andrew J. Medford, C. Franklin Goldsmith, Tejs Vegge, Joshua S. Weitz, Andrew A. Peterson
The fundamental models of epidemiology describe the progression of an infectious disease through a population using compartmentalized differential equations, but do not incorporate population-level heterogeneity in disease susceptibility.