Artificial intelligence and specifically machine learning applications are nowadays used in a
variety of scientific applications and cutting-edge technologies, where they have a …

In this review we will discuss recent advances in computational prediction of solubility in
water-based solvents. Our focus is set on recent advances in predictions of biorelevant …

Aqueous organic redox flow batteries offer an environmentally benign, tunable, and safe
route to large-scale energy storage. The energy density is one of the key performance …

We present a machine learning approach to automated force field development in
dissipative particle dynamics (DPD). The approach employs Bayesian optimization to …

In this study, we design and carry out a survey, asking human experts to predict the aqueous
solubility of druglike organic compounds. We investigate whether these experts, drawn …

Abstract 17817 compounds were collected from the Bradley open melting point data set,
including eight elements: C, H, O, N, F, S, Cl, Br, and I. An extended atom-based and bond …

A derivatization and air-assisted dispersive liquid-liquid microextraction procedure with
organic phase solidification on a paper template was developed for the first time. The …

We present an innovative method for predicting the dynamic electron correlation energy of
an atom or a bond in a molecule utilizing topological atoms. Our approach uses the machine …

We compare a range of computational methods for the prediction of sublimation
thermodynamics (enthalpy, entropy, and free energy of sublimation). These include a model …

The quantitative structure-activity relationship (QSAR) approach has been used in numerous
chemical compounds as in silico computational assessment for a long time. Further, owing …