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Nanoconfinement can drastically change the behavior of liquids, puzzling us with counterintuitive properties. Moreover, it is relevant in applications, including decontamination and crystallization control. It still lacks a systematic…
Recently, computational modeling has shifted towards the use of deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by…
Simulation of fluid flow in porous media has many applications, from the micro-scale (cell membranes, filters, rocks) to macro-scale (groundwater, hydrocarbon reservoirs, and geothermal) and beyond. Direct simulation of flow in porous media…
Global climate models represent small-scale processes such as clouds and convection using quasi-empirical models known as parameterizations, and these parameterizations are a leading cause of uncertainty in climate projections. A promising…
Superionic ice, where water molecules dissociate into a lattice of oxygen ions and a rapidly diffusing 'gas' of protons, represents an exotic state of matter with broad implications for planetary interiors and energy applications. Recently,…
Confinement has generally the effect of suppressing order in condensed matter. Indeed, phase transitions such as freezing, or the superfluid transition in liquid helium, occur at lower temperatures in confinement than they do in the bulk.…
Nanofluidics has firmly established itself as a new field in fluid mechanics, as novel properties have been shown to emerge in fluids at the nanometric scale. Thanks to recent developments in fabrication technology, artificial nanofluidic…
Bulk water presents a large number of crystalline and amorphous ices. Hydrophobic nanoconfinement is known to affect the tendency of water to form ice and to reduce the melting temperature. However, a systematic study of the ice phases in…
A good representation of mesoscopic fluids is required to combine with molecular simulations at larger length and time scales (De Fabritiis {\it et. al}, Phys. Rev. Lett. 97, 134501 (2006)). However, accurate computational models of the…
Polymer nanocomposites (PNCs) offer a broad range of thermophysical properties that are linked to their compositions. However, it is challenging to establish a universal composition-property relation of PNCs due to their enormous…
Modeling the unsaturated behavior of porous materials with multimodal pore size distributions presents significant challenges, as standard hydraulic models often fail to capture their complex, multi-scale characteristics. A common…
We investigate through non-equilibrium Molecular Dynamics simulations the structure and flow of fluids in functionalized nanopores. The nanopores are modeled as cylindrical structures with solvophilic and solvophobic sites. Two fluids are…
We present a versatile setup for investigating the nanofluidic behavior of nanoparticles as a function of the gap distance between two confining surfaces. The setup is designed as an open system which operates with small amounts of…
Machine learning techniques are powerful tools for construction of emulators for complex systems. We explore different machine learning methods and conceptual methodologies, ranging from functional approximations to dynamical…
Density profiles are the most common measure of inhomogeneous structure in confined fluids, but their connection to transport coefficients is poorly understood. We explore via simulation how tuning particle-wall interactions to flatten or…
Water plays a key role in biological membrane transport. In ion channels and water-conducting pores (aquaporins), one dimensional confinement in conjunction with strong surface effects changes the physical behavior of water. In molecular…
Fluids confined in nanopores exhibit properties different from the properties of the same fluids in bulk, among these properties are the isothermal compressibility or elastic modulus. The modulus of a fluid in nanopores can be extracted…
It is well stablished that confinement of supercooled liquids in nano-pores induces various effects as a strong modification of the dynamics and a layering of the local structure. In this work we raise the issue as how these confinement…
Machine Learning tools are nowadays widely applied extensively to the prediction of the properties of molecular materials, using datasets extracted from high-throughput computational models. In several cases of scientific and technological…
Nanoscale fluid transport is typically pictured in terms of atomic-scale dynamics, as is natural in the real-space framework of molecular simulations. An alternative Fourier-space picture, that involves the collective charge fluctuation…