In the next half-century, physical chemistry will likely undergo a profound transformation,
driven predominantly by the combination of recent advances in quantum chemistry and …

Neural network potentials have recently emerged as an efficient and accurate tool for
accelerating ab initio molecular dynamics (AIMD) in order to simulate complex condensed …

Quantum chemistry provides chemists with invaluable information, but the high
computational cost limits the size and type of systems that can be studied. Machine learning …

Unleashing the predictive power of molecular dynamics (MD), Neural Network Potentials
(NNPs) trained on Density Functional Theory (DFT) calculations are revolutionizing our …

Electrolyte solutions play a vital role in a vast range of important materials chemistry
applications. For example, they are a crucial component in batteries, fuel cells …

Deviation of sodium and potassium concentrations from their optimal values in living
organisms is associated with severe health conditions, thus their precise determination in …

Many drug molecules contain functional groups, resulting in a torsional barrier
corresponding to rotation around the bond linking the fragments. In medicinal chemistry and …

In this study, the correlated resolution-of-identity Møller–Plesset perturbation theory of
second order (RIMP2) ab initio level of theory has been combined with the newly …

Employing neutral impact collision ion scattering spectroscopy (NICISS), we have directly
measured the concentration depth profiles (CDPs) of various monovalent ions at the vapor …

The use of solvents is ubiquitous in chemistry. Empirical parameters, such as the Kamlet–
Taft parameters and Gutmann donor/acceptor numbers, have long been used to predict and …