Effective properties of functional materials crucially depend on their 3D microstructure. In this paper, we investigate quantitative relationships between descriptors of two-phase microstructures, consisting of solid and pores and their mass transport properties. To that end, we generate a vast database comprising 90,000 microstructures drawn from nine different stochastic models, and compute their effective diffusivity and permeability as well as various microstructural descriptors. To the best of our knowledge, this is the largest and most diverse dataset created for studying the influence of 3D microstructure on mass transport. In particular, we establish microstructure-property relationships using analytical prediction formulas, artificial (fully-connected) neural networks, and convolutional neural networks. Again, to the best of our knowledge, this is the first time that these three statistical learning approaches are quantitatively compared on the same dataset. The diversity of the dataset increases the generality of the determined relationships, and its size is vital for robust training of convolutional neural networks. We make the 3D microstructures, their structural descriptors and effective properties, as well as the code used to study the relationships between them available open access.

In traditional Li‐ion batteries, the electrolyte consists of a Li‐conducting salt dissolved in organic solvents at a concentration of 1 mol L⁻¹ (1 M). In this work, we use increased LiPF₆ concentrations between 1 and 2.3 M to investigate the influence of the electrolyte salt concentration on the rate capability of ultra‐thick (49.5 mg cm⁻²) and thin (5.6 mg cm⁻²) NCM 622 electrodes, respectively. At higher electrolyte salt concentrations than 1 M, thin electrodes suffer from increased polarization, due to a higher viscosity and a reduced ionic conductivity. In contrast, by raising the salt concentration from 1 to 1.9 M the discharge capacity of ultra‐thick electrodes is increased by more than 50 % for current densities above 3 mA cm⁻², which significantly improves their rate capability. 3D microstructure resolved simulations revealed that this effect results from the mitigation of Li‐ion depletion in the electrolyte filled pore space of ultra‐thick electrodes.

Most cathode materials for Li-ion batteries exhibit a low electronic conductivity. Therefore, a considerable amount of conductive additives is added during electrode production. A mixed phase of carbon and binder provides a 3D network for electron transport and at the same time improves the mechanical stability of the electrodes. However, this so-called carbon binder domain (CBD) hinders the transport of lithium ions through the electrolyte and reduces the specific energy of the cells. Therefore, the CBD content is an important design parameter for optimal battery performance. In the present study, stochastic 3D microstructure modeling, microstructure characterization, conductivity simulations as well as microstructure-resolved electrochemical simulations are performed to identify the influence of the CBD content and its spatial distribution on electrode performance. The electrochemical simulations on virtual, but realistic, electrode microstructures with different active material content and particle size distributions provide insights to limiting transport mechanisms and optimal electrode configurations. Furthermore, we use the results of both the microstructure characterization and electrochemical simulations to deduce extensions of homogenized cell models providing improved predictions of cell performance at low CBD contents relevant for high energy density batteries.

From simulating galaxy formation to viral transmission in a pandemic, scientific models play a pivotal role in developing scientific theories and supporting government policy decisions that affect us all. Given these critical applications, a poor modelling assumption or bug could have far-reaching consequences. However, scientific models possess several properties that make them notoriously difficult to test, including a complex input space, long execution times, and non-determinism, rendering existing testing techniques impractical. In fields such as epidemiology, where researchers seek answers to challenging causal questions, a statistical methodology known as Causal inference has addressed similar problems, enabling the inference of causal conclusions from noisy, biased, and sparse data instead of costly experiments. This article introduces the causal testing framework: a framework that uses causal inference techniques to establish causal effects from existing data, enabling users to conduct software testing activities concerning the effect of a change, such as metamorphic testing, a posteriori . We present three case studies covering real-world scientific models, demonstrating how the causal testing framework can infer metamorphic test outcomes from reused, confounded test data to provide an efficient solution for testing scientific modelling software.