Density functional theory describes matter at the quantum level, but all popular
approximations suffer from systematic errors that arise from the violation of mathematical …

The optimal tuning (OT) of range-separated hybrid (RSH) functionals has been proposed as
the currently most accurate DFT-based way to compute the relevant quantities required for …

The development of functional organic fluorescent materials calls for fast and accurate
predictions of photophysical parameters for processes such as high-throughput virtual …

Molecular-orbital-based machine learning (MOB-ML) provides a general framework for the
prediction of accurate correlation energies at the cost of obtaining molecular orbitals. The …

Over recent years, the use of statistical learning techniques applied to chemical problems
has gained substantial momentum. This is particularly apparent in the realm of physical …

Density functional theory-based high-throughput materials and drug discovery has achieved
tremendous success in recent decades, but its power on organic semiconducting molecules …

The vast chemical space of metal and ligand combinations in Transition Metal Complexes
(TMCs) gives rise to a rich variety of electronic excited states with local and non-local …

In this work, we develop an accurate and efficient XGBoost machine learning model for
predicting the global-density-dependent range-separation parameter, ω GDD, for long …

Virtual high-throughput screening (VHTS) and machine learning (ML) with density functional
theory (DFT) suffer from inaccuracies from the underlying density functional approximation …

The ability to predict transport properties of fluids, such as the self-diffusion coefficient and
viscosity, has been an ongoing effort in the field of molecular modeling. While there are …