An emulator for kinetic Monte Carlo simulations of kinetically controlled metal electrodeposition

In recent years there has been an increasing interest in developing models for atomistic- scale simulations of electrochemical metal deposition processes. One the most interesting and challenging features of such models is their capability to reproduce the physical evolution of the final deposition shape in a practical time scale. To date, most of the available models are limited to the simulation of a few nanoseconds of the physical phenomenon, for instance molecular dynamics, and there are very few methods, including kinetic Monte Carlo, that can reach to reproduce some seconds due to the requirement of an enormous computational cost. In this paper we present a surrogate-assisted kinetic Monte Carlo method based on Gaussian process emulation as a tool for predicting the final electrodeposition shape in a kinetically controlled copper electrodeposition on a gold substrate. The main advantage of this method is its ability to dramatically reduce the computational cost of the kinetic Monte Carlo simulation while yielding accurate results

Enhanced Gaussian Process Dynamical Models with Knowledge Transfer for Long-term Battery Degradation Forecasting

Predicting the end-of-life or remaining useful life of batteries in electric vehicles is a critical and challenging problem, predominantly approached in recent years using machine learning to predict the evolution of the state-of-health during repeated cycling. To improve the accuracy of predictive estimates, especially early in the battery lifetime, a number of algorithms have incorporated features that are available from data collected by battery management systems. Unless multiple battery data sets are used for a direct prediction of the end-of-life, which is useful for ball-park estimates, such an approach is infeasible since the features are not known for future cycles. In this paper, we develop a highly-accurate method that can overcome this limitation, by using a modified Gaussian process dynamical model (GPDM). We introduce a kernelised version of GPDM for a more expressive covariance structure between both the observable and latent coordinates. We combine the approach with transfer learning to track the future state-of-health up to end-of-life. The method can incorporate features as different physical observables, without requiring their values beyond the time up to which data is available. Transfer learning is used to improve learning of the hyperparameters using data from similar batteries. The accuracy and superiority of the approach over modern benchmarks algorithms including a Gaussian process model and deep convolutional and recurrent networks are demonstrated on three data sets, particularly at the early stages of the battery lifetime

Probabilistic sensitivity analysis for multivariate model outputs with applications to Li-ion batteries

Full battery models are highly complex, which limits their application to tasks such as optimization and uncertainty quantification. To lower the computational burden, sensitivity analysis (SA) can be used as a precursor to identify the most important parameters in the model, but SA itself relies on a high number of full model evaluations, which has motivated the use of emulators. For high-dimensional output problems, emulators are challenging to construct. In this paper we develop a probabilistic framework for SA of high-dimensional output models using a Gaussian process emulator based on dimensionality reduction. This allows us to perform SA under uncertainty for multi-ouput problems, providing error bounds for the emulator predictions of sensitivity measures. We show how this can be achieved using Monte Carlo sampling or possibly by using semi-analytical expressions with highly efficient sampling. Moreover, we can perform SA for multivariate outputs by ranking the sensitivity measures related to (uncorrelated) coefficients in a basis for the output space

Uncertainty quantification for flow and transport in highly heterogeneous porous media based on simultaneous stochastic model dimensionality reduction

Groundwater flow models are usually subject to uncertainty as a consequence of the random representation of the conductivity field. In this paper, we use a Gaussian process model based on the simultaneous dimension reduction in the conductivity input and flow field output spaces in order quantify the uncertainty in a model describing the flow of an incompressible liquid in a random heterogeneous porous medium. We show how to significantly reduce the dimensionality of the high-dimensional input and output spaces while retaining the qualitative features of the original model, and secondly how to build a surrogate model for solving the reduced-order stochastic model. A Monte Carlo uncertainty analysis on the full-order model is used for validation of the surrogate model

Rechargeable organic–air redox flow batteries

A rechargeable organic–air flow battery based on aqueous electrolytes is proposed and tests are conducted in a divided cell with a three-electrode configuration. Quinoxaline is used as the negative redox couple due to its low electrode potential of c.a. −0.9 V vs. Hg|HgO in aqueous electrolytes. High-surface-area nickel mesh and manganese-dioxide electrodes were employed for oxygen evolution and reduction, respectively, together with a low-cost hydroxide doped polybenzimidazole (m-PBI) separator (c.a. 20 μm). In typical alkaline electrolytes (2 M NaOH), the open-circuit voltage of the flow battery was c.a. 0.95 V, which is comparable to existing organic-based batteries. The average charge and discharge cell voltage ranges at 5–10 mA cm−2 were 1.7–1.95 V and 0.4–0.7 V, respectively. Despite using low-cost materials, average coulombic and energy efficiencies of the batteries were c.a. 81 and 25%, respectively, at 7.5 mA cm−2 over 20 cycles

A surrogate modelling approach based on nonlinear dimension reduction for uncertainty quantification in groundwater flow models

In this paper, we develop a surrogate modelling approach for capturing the output field (e.g., the pressure head) from groundwater flow models involving a stochastic input field (e.g., the hy- draulic conductivity). We use a Karhunen-Lo`eve expansion for a log-normally distributed input field, and apply manifold learning (local tangent space alignment) to perform Gaussian process Bayesian inference using Hamiltonian Monte Carlo in an abstract feature space, yielding outputs for arbitrary unseen inputs. We also develop a framework for forward uncertainty quantification in such problems, including analytical approximations of the mean of the marginalized distri- bution (with respect to the inputs). To sample from the distribution we present Monte Carlo approach. Two examples are presented to demonstrate the accuracy of our approach: a Darcy flow model with contaminant transport in 2-d and a Richards equation model in 3-d

Manifold learning for the emulation of spatial fields from computational models

Repeated evaluations of expensive computer models in applications such as design optimization and uncertainty quantification can be computationally infeasible. For partial differential equation (PDE) models, the outputs of interest are often spatial fields leading to high-dimensional output spaces. Although emulators can be used to find faithful and computationally inexpensive approximations of computer models, there are few methods for handling high-dimensional output spaces. For Gaussian process (GP) emulation, approximations of the correlation structure and/or dimensionality reduction are necessary. Linear dimensionality reduction will fail when the output space is not well approximated by a linear subspace of the ambient space in which it lies. Manifold learning can overcome the limitations of linear methods if an accurate inverse map is available. In this paper, we use kernel PCA and diffusion maps to construct GP emulators for very high-dimensional output spaces arising from PDE model simulations. For diffusion maps we develop a new inverse map approximation. Several examples are presented to demonstrate the accuracy of our approach

The separator-divided soluble lead flow battery

The soluble lead flow battery (SLFB) is conventionally configured with an undivided cell chamber. This is possible, unlike other flow batteries, because both electrode active materials are electroplated as solids from a common species, Pb2+, on the electrode surfaces during charging. Physically separating the active materials has the advantage that a single electrolyte and pump circuit can be used; however, failure mechanisms such as electrical shorting may be observed. In addition, a common electrolyte requires that any electrolyte additives are compatible with both half-cell reactions. This paper introduces two new configurations; semi- and fully divided for the SLFB. Cationic, anionic, and microporous separators are assessed for ionic conductivity in SLFB electrolytes, showing that their incorporation adds as little as a 20 mV to the cell voltage. Voltammetry shows the effect of additives on the equilibrium potential and stripping overpotential of PbO2. It is then demonstrated that the incorporation of a separator into the SLFB can reduce failure due to electrical shorting and permit electrode-specific additives to be used. A unit flow cell with electrode area of 100 cm2 is shown to operate for over 300 Ah in the semi-divided configuration, more than doubling the previously reported cycle life for cells of similar size

Membrane-less organic-inorganic aqueous flow batteries with improved cell potential

A membrane-less organic-inorganic flow battery based on zinc and quinone species is proposed. By virtue of the slow dissolution rate of the deposited anode (<11.5 mg h-1 cm-2), the battery has a cell voltage of ca. 1.52 V with an average energy efficiency of ca. 73 at 30 mA cm-2 over 12 cycles