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Found 5 papers, 2 papers with code
Transition1x -- a Dataset for Building Generalizable Reactive Machine Learning Potentials
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.
NeuralNEB -- Neural Networks can find Reaction Paths Fast
We also compare with and outperform Density Functional based Tight Binding (DFTB) on both accuracy and computational resource.
Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
Electron density $\rho(\vec{r})$ is the fundamental variable in the calculation of ground state energy with density functional theory (DFT).
Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networks
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.
DeepDFT: Neural Message Passing Network for Accurate Charge Density Prediction
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.
