Related papers: Probing the Temporal Response of Liquid Water to a…
The dynamics of ultrafast energy transfer to water clusters and to bulk water by a highly intense, sub-cycle THz pulse of duration $\approx$~150~fs is investigated in the context of force-field molecular dynamics simulations. We focus our…
Energy dissipation in water is very fast and more efficient than in many other liquids. This behavior is commonly attributed to the intermolecular interactions associated with hydrogen bonding. Here, we investigate the dynamic energy flow…
The last decade has witnessed a rapid advancement in laser technology, enabling the direct monitoring and control of electronic motion on its natural attosecond to sub-femtosecond timescales. Ultrafast processes are conventionally studied…
Dispersion of collective excitations in fluid Hydrogen along the isothermal line T=2500~K, including the region of molecular-to-atomis fluid transition, is studied by ab initio molecular dynamics (AIMD) simulations. The obtained density…
Absorption spectra of liquid water at 300 K are calculated from both classical and density functional theory molecular dynamics simulation data, which together span from 1 MHz to hundreds of THz, agreeing well with experimental data…
Infrared pump-probe spectroscopy provides detailed information on the dynamics of hydrogen-bonded liquids. Due to dissipation of the absorbed pump pulse energy, also thermal equilibration dynamics contributes to the observed signal.…
We present a multiscale simulation approach for hydroxide transport in aqueous solutions of potassium hydroxide, combining ab initio molecular dynamics (AIMD) simulations with force field ensemble averaging and lattice Monte Carlo…
Conventional molecular dynamics (MD) simulation approaches, such as $\textit{ab initio}$ MD (AIMD) and empirical force field MD (EFFMD), face significant trade-offs between physical accuracy and computational efficiency. This work presents…
Motivated by the very low diffusivity recently found in ab initio simulations of liquid water, we have studied its dependence with temperature, system size, and duration of the simulations. We use ab initio molecular dynamics (AIMD),…
The vibrational motion of molecules in dissipative environments, such as solvation and protein molecules, is composed of contributions from both intermolecular and intramolecular modes. The existence of these collective modes introduces…
Liquid water is one of the most studied substances, yet many of its properties are difficult to rationalize. The uniqueness of water is rooted in the dynamic network of hydrogen-bonded molecules with relaxation time constants of about one…
Vibrational spectroscopy is a key technique to elucidate microscopic structure and dynamics. Without the aid of theoretical approaches, it is however, often difficult to understand such spectra at a microscopic level. Ab initio molecular…
We describe a new approach based on semiclassical molecular dynamics that allows to simulate infrared absorption or emission spectra of molecular systems with inclusion of anharmonic intensities. This is achieved from semiclassical power…
Numerical studies of ionic motion through solid electrolytes commonly involve static nudged-elastic band (NEB) methods or costly \emph{ab initio} molecular dynamics (AIMD). Building on a time-local model of current carrier-electrolyte…
Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex aqueous systems such as solid-liquid interfaces. Here, we present a machine learning…
Machine-learning interatomic potentials (MLPs) are fast, data-driven surrogate models of atomistic systems' potential energy surfaces that can accelerate ab-initio molecular dynamics (MD) simulations by several orders of magnitude. The…
Tracking the excitation of water molecules in the homogeneous liquid is challenging due to the ultrafast dissipation of rotational excitation energy through the hydrogen-bonded network. Here we demonstrate strong transient anisotropy of…
The in silico exploration of chemical, physical and biological systems requires accurate and efficient energy functions to follow their nuclear dynamics at a molecular and atomistic level. Recently, machine learning tools gained a lot of…
Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for…
Accounting for electrons and nuclei simultaneously is a powerful capability of ab initio molecular dynamics (AIMD). However, AIMD is often unable to accurately reproduce properties of systems such as water due to inaccuracies in the…