Pavlo O. Dral

TL;DR: This data set provides quantum chemical properties for a relevant, consistent, and comprehensive chemical space of small organic molecules that may serve the benchmarking of existing methods, development of new methods, such as hybrid quantum mechanics/machine learning, and systematic identification of structure-property relationships.

TL;DR: The transferability of the approach is demonstrated, using semiempirical quantum chemistry and machine learning models trained on 1 and 10% of 134k organic molecules, to reproduce enthalpies of all remaining molecules at density functional theory level of accuracy.

TL;DR: A view on the current state of affairs in this new exciting research field is offered, challenges of using ML in QC applications are described, and potential future developments are outlined.

TL;DR: The underlying theoretical formalism of the OMx methods and their implementation in full detail is described, and all relevant OMx parameters for hydrogen, carbon, nitrogen, oxygen, and fluorine are reported.

TL;DR: This work shows that deep learning can be used for exploring complex and highly nonlinear multistate potential energy surfaces of polyatomic molecules and related nonadiabatic dynamics, and demonstrates that the results from nonadiABatic dynamics run with the DNN models are very close to those from the dynamicsRun with the pure ab initio method.