Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …

Implicit solvation is an effective, highly coarse-grained approach in atomic-scale simulations
to account for a surrounding liquid electrolyte on the level of a continuous polarizable …

Designing and screening novel electrocatalysts, understanding electrocatalytic mechanisms
at an atomic level, and uncovering scientific insights lie at the center of the development of …

Recently, machine learning (ML) has been used to address the computational cost that has
been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural …

Due to the unique properties of carbon nanotubes (CNTs), the electrical and thermal
conductivity of CNT-modified polymeric composites (CNTMPCs) can be manipulated and …

Interatomic potentials approximate the potential energy of atoms as a function of their
coordinates. Their main application is the effective simulation of many-atom systems. Here …

Chemical surfactants are omnipresent in consumer products, but they are the subject of
environmental concerns. For this reason, the complete replacement of petrochemical …

The prediction of thermophysical properties at extreme conditions is an important application
of molecular simulations. The quality of these predictions primarily depends on the quality of …

Molecular Dynamics (MD) simulation is a powerful tool for understanding the dynamics and
structure of matter. Since the resolution of MD is atomic-scale, achieving long timescale …