Artificial Neural Networks (NN) are already heavily involved in methods and applications for
frequent tasks in the field of computational chemistry such as representation of potential …

Deep neural network (DNN) potentials have recently gained popularity in computer
simulations of a wide range of molecular systems, from liquids to materials. In this study, we …

Density functional theory (DFT) has been applied to modeling molecular interactions in
water for over three decades. The ubiquity of water in chemical and biological processes …

Liquid electrolytes are one of the most important components of Li-ion batteries, which are a
critical technology of the modern world. However, we still lack the computational tools …

Solvent effects influence all stages of the chemical processes, modulating the stability of
intermediates and transition states, as well as altering reaction rates and product ratios …

Developing machine learning-based interatomic potentials from ab initio electronic structure
methods remains a challenging task for computational chemistry and materials science. This …

We present a generalization of the many-body energy (MB-nrg) theoretical/computational
framework that enables the development of data-driven potential energy functions (PEFs) for …

In this paper, we investigate the performance of different machine learning potentials (MLPs)
in predicting key thermodynamic properties of water using RPBE+ D3. Specifically, we …

As the most important solvent, water has been at the center of interest since the advent of
computer simulations. While early molecular dynamics and Monte Carlo simulations had to …

Context. Energy redistribution after a chemical reaction is one of the few mechanisms that
can explain the diffusion and desorption of molecules which require more energy than the …