Related papers: MOSP: A User-interface Package for Simulating Meta…
Material characterization in nano-mechanical tests requires precise interatomic potentials for the computation of atomic energies and forces with near-quantum accuracy. For such purposes, we develop a robust neural-network interatomic…
Numerical simulation of plasma discharges is often performed by models developed in-house and coupling externally and separately written codes. The MOOSE (Multiphysics Object Oriented Simulation Environment) framework provides tools for…
The presence of open active metal sites in Metal-Organic Frameworks (MOFs) exhibit higher catalytic activity. However, rational accomplishment of MOFs in heterogeneous catalysis is limited due to coordination bonds. Recently balanced…
MoS2 nanostructures are promising catalysts for proton-exchange-membrane (PEM) electrolyzers to replace expensive noble metals. Their broadscale application demands high activity for the hydrogen evolution reaction (HER) as well as robust…
The objective of this work is to assess computationally efficient coarse-grained plasticity models against high-fidelity crystal plasticity simulations for magnesium polycrystals over a wide range of textures and grain sizes. A basic…
Machine learning interatomic potentials (MLIPs) have become widely used tools in atomistic simulations. For much of the history of this field, the most commonly employed architectures were based on short-ranged atomic energy contributions,…
Soft porous crystals present a challenge to molecular dynamics simulations with flexible size and shape of the simulation cell (i.e., in the NPT ensemble), since their framework responds very sensitively to small external stimuli. Hence,…
Solid-liquid interfaces are at the heart of many modern-day technologies and provide a challenge to many materials simulation methods. A realistic first-principles computational study of such systems entails the inclusion of solvent…
In situ NMR spectroscopy is a powerful technique to investigate charge storage mechanisms in carbon-based supercapacitors thanks to its ability to distinguish ionic and molecular species adsorbed in the porous electrodes from those in the…
The mechanisms of physical and chemical interactions of low temperature plasmas with surfaces can be fruitfully explored using molecular dynamics (MD) simulations. MD simulations follow the detailed motion of sets of interacting atoms…
Boron phosphide (BP) is a (super)hard semiconductor constituted of light elements, which is promising for high demand applications at extreme conditions. The behavior of BP at high temperatures and pressures is of special interest but is…
Recent years have seen a large increase in the number of reported framework materials, including the nowadays-ubiquitous metal-organic frameworks (MOFs). Many of these materials show flexibility and stimuli-responsiveness, i.e. their…
Metal-Organic Frameworks (MOFs) are materials with a high degree of porosity that can be used for applications in energy storage, water desalination, gas storage, and gas separation. However, the chemical space of MOFs is close to an…
Markov state models (MSMs) are a powerful tool to analyze and coarse-grain complex dynamical data into interpretable kinetic processes. This capability is particularly important in heterogeneous catalysis, where a medley of reactants and…
Soft materials (e.g., enveloped viruses, liposomes, membranes and supercooled liquids) simultaneously deform or display collective behaviors, while undergoing atomic scale vibrations and collisions. While the multiple space-time character…
In recent years, machine learning interatomic potentials (MLIPs) have attracted significant attention as a method that enables large-scale, long-time atomistic simulations while maintaining accuracy comparable to electronic structure…
Though offering unprecedented pathways to molecular dynamics (MD) simulations of technologically-relevant materials and conditions, machine-learning interatomic potentials (MLIPs) are typically trained for ``simple'' materials and…
Machine learned interatomic potentials (MLIPs) have emerged as powerful tools for molecular dynamics (MD) simulations with their competitive accuracy and computational efficiency. However, MLIPs are often observed to exhibit un-physical…
The development of supercapacitors is impeded by the unclear relationships between nanoporous electrode structures and electrochemical performance, primarily due to challenges in decoupling the complex interdependencies of various…
Simulating interactions between non-spherical colloidal particles is computationally challenging due to the complex dependency of forces and energies on their geometry. We introduce and evaluate both descriptor-based and end-to-end models…