Transferability of data-driven, many-body models for CO2 simulations in the vapor and liquid phases

Extending on previous work by Riera et al. [J. Chem. Theory Comput. 16, 2246 (2020)], we introduce a second generation family of data-driven many-body MB-nrg models for CO2 and systematically assess how the strength and anisotropy of the CO2-CO2 interactions affect the models' ability to predict vapor, liquid, and vapor-liquid equilibrium properties. Building upon the many-body expansion formalism, we construct a series of MB-nrg models by fitting 1-body and 2-body reference energies calculated at the coupled cluster level of theory for large monomer and dimer training sets. Advancing from the first generation models, we employ the Charge Model 5 scheme to determine the atomic charges and systematically scale the 2-body energies to obtain more accurate descriptions of vapor, liquid, and vapor-liquid equilibrium properties. Comparisons with the polarizable TTM-nrg model, which is constructed from the same training sets as the MB-nrg models but using a simpler representation of short-range interactions based on conventional Born-Mayer functions, showcase the necessity of high dimensional functional forms for an accurate description of the multidimensional energy landscape of liquid CO2. These findings emphasize the key role played by the training set quality and flexibility of the fitting functions in the development of transferable, data-driven models which, accurately representing high-dimensional many-body effects, can enable predictive computer simulations of molecular fluids across the entire phase diagram.

Machine learning of lubrication correction based on GPR for the coupled DPD-DEM simulation of colloidal suspensions

Hydrodynamic interactions have a major impact on the suspension properties, but they are absent in atomic and molecular fluids due to a lack of intervening medium at close range. To...

Early stages of aggregation in fluid mixtures of dimers and spheres: a theoretical and simulation study

We use Monte Carlo simulation and the Reference Interaction Site Model (RISM) theory of molecular fluids to investigate a simple model of colloidal mixture consisting of dimers, made up of...

Towards a machine learned thermodynamics: exploration of free energy landscapes in molecular fluids, biological systems and for gas storage and separation in metal–organic frameworks

Combined machine learning-molecular simulations protocols for the prediction and exploration of free energy surfaces.

Wetting transition of ionic liquids at metal surfaces: A computational molecular approach to electronic screening using a virtual Thomas-Fermi fluid

Of particular relevance to energy storage, electrochemistry and catalysis, ionic and dipolar liquids display a wealth of unexpected fundamental behaviors – in particular in confinement. Beyond now well-documented adsorption, overscreening and crowding effects1,2,3, recent experiments have highlighted novel phenomena such as unconventional screening4 and the impact of the electronic nature – metallic versus insulating – of the confining surface on wetting/phase transitions5,6. Such behaviors, which challenge existing theoretical and numerical modeling frameworks, point to the need for new powerful tools to embrace the properties of confined ionic/dipolar liquids. Here, we introduce a novel atom-scale approach which allows for a versatile description of electronic screening while capturing all molecular aspects inherent to molecular fluids in nanoconfined/interfacial environments. While state of the art molecular simulation strategies only consider perfect metal or insulator surfaces, we build on the Thomas-Fermi formalism for electronic screening to develop an effective approach that allows dealing with any imperfect metal between these asymptotes. The core of our approach is to describe electrostatic interactions within the metal through the behavior of a `virtual' Thomas-Fermi fluid of charged particles, whose Debye length sets the Thomas-Fermi screening length λ in the metal. This easy-to-implement molecular method captures the electrostatic interaction decay upon varying λ from insulator to perfect metal conditions, while describing very accurately the capacitance behavior – and hence the electrochemical properties – of the metallic confining medium. By applying this strategy to a nanoconfined ionic liquid, we demonstrate an unprecedented wetting transition upon switching the confining medium from insulating to metallic. This novel approach provides a powerful framework to predict the unsual behavior of ionic liquids, in particular inside nanoporous metallic structures, with direct applications for energy storage and electrochemistry.

Diffraction in the Static Approximation

The basic principles of crystallography are reviewed, including the lattice, basis and reciprocal lattice. The Bragg diffraction law and Laue equation, which describe coherent scattering from a crystalline material, are derived, and the structure factor and differential cross-section are obtained in the static approximation. It is explained how the presence of defects, short-range order, and reduced dimensionality causes diffuse scattering. For non-crystalline materials, such as liquids and glasses, the pair distribution function and density-density correlation function are introduced, and their relation to the static structure factor established. For molecular fluids, the form factor is defined and calculated for a diatomic molecule, and the separation of intra- and inter-molecular scattering is discussed. The principles of small-angle neutron scattering are described.