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The deep energy method (DEM) has been used to solve the elastic deformation of structures with linear elasticity, hyperelasticity, and strain-gradient elasticity material models based on the principle of minimum potential energy. In this…
Programmable electron-beam scanning offers new opportunities to improve dose efficiency and suppress scan-induced artifacts in scanning transmission electron microscopy. Here, we systematically benchmark the impact of non-raster…
Molecular dynamics simulations have been used extensively to determine equilibrium properties of the electrode-electrolyte interface in supercapacitors held at various potentials. While such studies are essential to understand and optimize…
The exponential computational cost of describing strongly correlated electrons can be mitigated by adopting a reduced density-matrix (RDM)-based description of the electronic structure. While variational two-electron RDM (v2RDM) methods can…
Accurate parameter estimation in electrochemical battery models is essential for monitoring and assessing the performance of lithium-ion batteries (LiBs). This paper presents a novel approach that combines deep reinforcement learning (DRL)…
We present a differentiable formalism for learning free energies that is capable of capturing arbitrarily complex model dependencies on coarse-grained coordinates and finite-temperature response to variation of general system parameters.…
We investigate the photoionization dynamics of atoms subjected to intense, ultrashort laser pulses through the use of quantum trajectories. This method provides a unique and consistent framework for examining electron dynamics within a…
Energy storage and release in dielectric materials can be described on the basis of the charge trapping mechanism. Most phenomenological aspects have been recently rationalized in terms of the space charge model~\cite{blaise,blaise1}.…
Existing molecular machine learning force fields (MLFFs) generally focus on the learning of atoms, molecules, and simple quantum chemical properties (such as energy and force), but ignore the importance of electron density (ED) $\rho(r)$ in…
A coarse-grained simulation method to predict NMR spectra of ions diffusing in porous carbons is proposed. The coarse-grained model uses input from molecular dynamics simulations such as the free-energy profile for ionic adsorption, and…
Molecular dynamics simulations are a powerful tool to study diffusion processes in battery electrolyte and electrode materials. From a single molecular dynamics simulation many properties relevant to diffusion can be obtained, including the…
Marcus theory is fundamental to describing electron transfer reactions and quantifying their rates, effectively representing the energy surface associated with an electron transfer from the reactant to the product ionic state via parabolas…
The interaction of ion beams with matter includes the investigation of the basic principles of ion stopping in heated materials. An unsolved question is the effect of different, especially higher, ion beam fluences on ion stopping in solid…
Within the MNPBEM toolbox, we show how to simulate electron energy loss spectroscopy (EELS) of plasmonic nanoparticles using a boundary element method approach. The methodology underlying our approach closely follows the concepts developed…
We show that the exact exchange-correlation potential of time-dependent density-functional theory displays dynamical step structures that have a spatially non-local and time non-local dependence on the density. Using one-dimensional…
A new 1D divertor plasma code, SD1D, has been used to examine the role of recombination, radiation, and momentum exchange in detachment. Neither momentum or power losses by themselves are found to be sufficient to produce a reduction in…
Highly energetic ions that impact materials have applications from semiconductor industry to medicine, and are fundamentally interesting as they trigger multi-length and time-scale processes. In particular, they excite electrons into…
This article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability problems in power systems. Recent advances show promising results in model-free DRL-based…
The rate performance of any electrode or solid electrolyte material used in a battery is critically dependent on the migration barrier ($E_m$) governing the motion of the intercalant ion, which is a difficult-to-estimate quantity both…
The physical foundations of the dissipation of energy and the associated heating in weakly collisional plasmas are poorly understood. Here, we compare and contrast several measures that have been used to characterize energy dissipation and…