Molecular quantum chemical data sets and databases for machine learning potentials
The field of computational chemistry is increasingly leveraging machine learning (ML)
potentials to predict molecular properties with high accuracy and efficiency, providing a …
potentials to predict molecular properties with high accuracy and efficiency, providing a …
Improving the reliability of, and confidence in, DFT functional benchmarking through active learning
Validating the performance of exchange-correlation functionals is vital to ensure the
reliability of density functional theory (DFT) calculations. Typically, these validations involve …
reliability of density functional theory (DFT) calculations. Typically, these validations involve …
Towards comprehensive coverage of chemical space: Quantum mechanical properties of 836k constitutional and conformational closed shell neutral isomers …
The Vector-QM24 (VQM24) dataset attempts to more comprehensively cover all possible
neutral closed shell small organic and inorganic molecules and their conformers at state of …
neutral closed shell small organic and inorganic molecules and their conformers at state of …
Adaptive atomic basis sets
Atomic basis sets are widely employed within quantum mechanics based simulations of
matter. We introduce a machine learning model that adapts the basis set to the local …
matter. We introduce a machine learning model that adapts the basis set to the local …
Targeting spectroscopic accuracy for dispersion bound systems from ab initio techniques: translational eigenstates of Ne@C
We investigate the endofullerene system Ne@ C $ _ {70} $, by constructing a three-
dimensional Potential Energy Surface (PES) describing the translational motion of the Ne …
dimensional Potential Energy Surface (PES) describing the translational motion of the Ne …