For exploration of chemical and biological systems, the combined quantum mechanics and
molecular mechanics (QM/MM) and machine learning (ML) models have been developed …

Despite recent advances in the development of machine learning potentials (MLPs) for
biomolecular simulations, there has been limited effort on developing stable and accurate …

We examine methods to reweight classical molecular mechanics solvation calculations to
more expensive QM/MM energy functions. We first consider the solvation free energy …

Due to the higher computational cost relative to pure molecular mechanical (MM)
simulations, hybrid quantum mechanical/molecular mechanical (QM/MM) free energy …

The use of the most accurate (ie, QM or QM/MM) levels of theory for free energy simulations
(FES) is typically not possible. Primarily, this is because the computational cost associated …

Direct molecular dynamics (MD) simulation with ab initio quantum mechanical and
molecular mechanical (QM/MM) methods is very powerful for studying the mechanism of …

Quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations
have been developed to simulate molecular systems, where an explicit description of …

Among various models that incorporate solvation effects into first-principles-based electronic
structure theory such as density functional theory (DFT), the average solvent electrostatic …

Machine learning potentials (MLPs) are widely applied as an efficient alternative way to
represent potential energy surfaces (PESs) in many chemical simulations. The MLPs are …

While the use of machine-learning (ML) techniques is well established in cheminformatics
for the prediction of physicochemical properties and binding affinities, the training of ML …