The space-charge layer (SCL) phenomenon in Li +ion-conducting solid-state electrolytes (SSEs) is gaining much interest in different fields of solid-state ionics. Not only do SCLs influence charge-transfer resistance in all-solid-state batteries but also are analogous to their electronic counterpart in semiconductors; they could be used for Li + -ionic devices. However, the rather "elusive" nature of these layers, which occur on the nanometer scale and with only small changes in concentrations, makes them hard to fully characterize experimentally. Theoretical considerations based on either electrochemical or thermodynamic models are limited due to missing physical, chemical, and electrochemical parameters. In this work, we use kinetic Monte Carlo (kMC) simulations with a small set of input parameters to model the spatial extent of the SCLs. The predictive power of the kMC model is demonstrated by finding a critical range for each parameter in which the space-charge layer growth is significant and must be considered in electrochemical and ionic devices. The time evolution of the charge redistribution is investigated, showing that the SCLs form within 500 ms after applying a bias potential.

Kinetic Monte Carlo (kMC) simulations are frequently used to study (electro-)chemical processes within science and engineering. kMC methods provide insight into the interplay of stochastic processes and can link atomistic material properties with macroscopic characteristics. Significant problems concerning the computational demand arise if processes with large time disparities are competing. Acceleration algorithms are required to make slow processes accessible. Especially, the accelerated superbasin kMC (AS-kMC) scheme has been frequently applied within chemical reaction networks. For larger systems, the computational overhead of the AS-kMC is significant as the computation of the superbasins is done during runtime and comes with the need for large databases. Here, we propose a novel acceleration scheme for diffusion and transport processes within kMC simulations. Critical superbasins are detected during the system initialization. Scaling factors for the critical rates within the superbasins, as well as a lower bound for the number of sightings, are derived. Our algorithm exceeds the AS-kMC in the required simulation time, which we demonstrate with a 1D-chain example. In addition, we apply the acceleration scheme to study the time-of-flight (TOF) of charge carriers within organic semiconductors. In this material class, time disparities arise due to a significant spread of transition rates. The acceleration scheme allows a significant acceleration up to a factor of 65 while keeping the error of the TOF values negligible. The computational overhead is negligible, as all superbasins only need to be computed once.

Kinetic Monte Carlo (kMC) simulations are a popular tool to investigate the dynamic behavior of stochastic systems. However, one major limitation is their relatively high computational costs. In the last three decades, significant effort has been put into developing methodologies to make kMC more efficient, resulting in an enhanced runtime efficiency. Nevertheless, kMC models remain computationally expensive. This is in particular an issue in complex systems with several unknown input parameters where often most of the simulation time is required for finding a suitable parametrization. A potential route for automating the parametrization of kinetic Monte Carlo models arises from coupling kMC with a data-driven approach. In this work, we equip kinetic Monte Carlo simulations with a feedback loop consisting of Gaussian Processes (GPs) and Bayesian optimization (BO) to enable a systematic and data-efficient input parametrization. We utilize the results from fast-converging kMC simulations to construct a database for training a cheap-to-evaluate surrogate model based on Gaussian processes. Combining the surrogate model with a system-specific acquisition function enables us to apply Bayesian optimization for the guided prediction of suitable input parameters. Thus, the amount of trial simulation runs can be considerably reduced facilitating an efficient utilization of arbitrary kMC models. We showcase the effectiveness of our methodology for a physical process of growing industrial relevance: the space-charge layer formation in solid-state electrolytes as it occurs in all-solid-state batteries. Our data-driven approach requires only 1–2 iterations to reconstruct the input parameters from different baseline simulations within the training data set. Moreover, we show that the methodology is even capable of accurately extrapolating into regions outside the training data set which are computationally expensive for direct kMC simulation. Concluding, we demonstrate the high accuracy of the underlying surrogate model via a full parameter space investigation eventually making the original kMC simulation obsolete.

Kinetic Monte Carlo (kMC) simulations are a well-established tool for investigating the operation of electrochemical systems. Standard kMC algorithms become unfeasible in the presence of processes on vastly different time scales. In electrochemical systems, such time scale disparities often arise between fast transport processes and slow electrochemical reactions. A promising approach to overcome time scale disparities in kMC models is given by temporal acceleration schemes. In this work, we present a local temporal acceleration scheme to bridge the time scale disparity between fast transport and slow reaction dynamics. We combine the superbasin concept with a local, particle-based criterion for the quasi-equilibrium detection and a partitioning of transitions and particles in the system into process chains. Scaling of entire quasi-equilibrated process chains considerably reduces the computational effort without disturbing the relative dynamics of transitions within a process chain. The methodology is outlined for a hybrid organic–aqueous electrolyte device which links fast electronic processes in an organic semiconductor with slow reduction reactions at its interface to the electrolyte. Our approach captures local inhomogeneities such that local physical quantities can be reproduced accurately. Additionally, we show that previous accelerated superbasin algorithms are limited by the presence of spatially varying time scale disparities. Our algorithm achieves an acceleration of several orders of magnitude providing a serious alternative to replace existing multiscale models by stand-alone kMC simulations.