Machine learning (ML) potentials are a powerful tool in molecular modeling, enabling ab
initio accuracy for comparably small computational costs. Nevertheless, all-atom simulations …

The accuracy of computational models of water is key to atomistic simulations of
biomolecules. We propose a computationally efficient way to improve the accuracy of the …

The solvation properties of molecules, often estimated using quantum chemical simulations,
are important in the synthesis of energy storage materials, drugs, and industrial chemicals …

The dynamical behavior of small molecules in their environment can be studied with
classical molecular dynamics (MD) simulations to gain deeper insight on an atomic level …

Free energies govern the behavior of soft and liquid matter, and improving their predictions
could have a large impact on the development of drugs, electrolytes, or homogeneous …

The prediction of a molecule's solvation Gibbs free (Δ G solv) energy in a given solvent is an
important task which has traditionally been carried out via quantum chemical continuum …

Prediction of aqueous solubilities or hydration free energies is an extensively studied area in
machine learning applications in chemistry since water is the sole solvent in the living …

We report a method to predict the absolute solvation free energy (SFE) of small organic and
druglike molecules in water, carbon tetrachloride and chloroform solvents beyond 298 K by …

Accurate modeling of the solvent environment for biological molecules is crucial for
computational biology and drug design. A popular approach to achieve long simulation time …

Many challenges persist in developing accurate computational models for predicting
solvation free energy (Δ G sol). Despite recent developments in Machine Learning (ML) …