How Machine Learning Will Revolutionize Electrochemical Sciences

Abstract

Electrochemical systems function via interconversion of electric charge and chemical species and represent promising technologies for our cleaner, more sustainable future. However, their development time is fundamentally limited by our ability to identify new materials and understand their electrochemical response. To shorten this time frame, we need to switch from the trial-and-error approach of finding useful materials to a more selective process by leveraging model predictions. Machine learning (ML) offers data-driven predictions and can be helpful. Herein we ask if ML can revolutionize the development cycle from decades to a few years. We outline the necessary characteristics of such ML implementations. Instead of enumerating various ML algorithms, we discuss scientific questions about the electrochemical systems to which ML can contribute.

Figure 1

Research, development, and deployment tasks in any electrochemical system involve fundamentally four why questions. Each implicitly identifies the length and time scales of interest, thus specifying how to answer these questions using experiments and modeling as the tools. The sub-figures in the bottom panel are drawn as modules of energy storage systems and can be used to represent equivalent examples of other electrochemical systems. [Reprinted with permission from ref (5). Copyright 2020 The Electrochemical Society.]

Figure 2

(a) Example of a workflow coupling experimental data, a surrogate electrode mesostructure predictor, and ML (Sure Independent Screening and Sparsifying Operator) to predict the impact of electrode composition, initial porosity, and calendered pressure on the electrode tortuosity factor. [Reprinted with permission from ref (39). Copyright 2020 Elsevier.] (b) Example of a classification machine learning algorithm (Support Vector Machine) able to predict the impact of the percentage of NMC active material, solid-to-liquid ratio, and viscosity of the slurry on the final porosity of a lithium ion battery positive electrode. [Reprinted with permission from ref (38). Copyright 2019 Wiley-VCH GmbH.]

Figure 3

(a–c) Comparison between human (b) and CNN (c) segmentations of 3D XCT images. (d) Bayesian CNNs used to quantify the uncertainty in image segmentations. (e, f) Application of GANs to create unique, yet realistic, mesostructures.

Figure 4

(a) Measured electrode performance is interpreted using (b) physics-based electrochemical description. (c) The difference between the two is mapped in terms of mesostructure properties using data-driven modeling. The most representative properties are retrieved using this error landscape. (d) Experiments and predictions using interpreted mesostructure properties are shown to illustrate reliability of analysis. [Used with permission from Mistry et al., ref (23).]

Figure 5

Data-dependent characteristics of ML are illustrated by learning D(T) relation from discrete datapoints using two NN representations (with Sigmoid activation functions) shown in the insets. Columns represent different data complexity, while rows express model complexity. The solid red line is the trained model in each plot.