Machine Learning-Aided First-Principles Calculations of Redox Potentials

Redox potentials of electron transfer reactions are of fundamental importance for the performance and description of electrochemical devices. Despite decades of research, accurate computational predictions for the redox potential of even simple metals remain very challenging. Here we use a combination of first principles calculations and machine learning to predict the redox potentials of three redox couples, $\mathrm{Fe}^{2+}$/$\mathrm{Fe}^{3+}$, $\mathrm{Cu}^{+}$/$\mathrm{Cu}^{2+}$ and $\mathrm{Ag}^{+}$/$\mathrm{Ag}^{2+}$. Using a hybrid functional with a fraction of 25\% exact exchange (PBE0) the predicted values are 0.92, 0.26 and 1.99 V in good agreement with the best experimental estimates (0.77, 0.15, 1.98 V). We explain in detail, how we combine machine learning, thermodynamic integration from machine learning to semi-local functionals, as well as a combination of thermodynamic perturbation theory and $\Delta$-machine learning to determine the redox potentials for computationally expensive hybrid functionals. The combination of these approaches allows one to obtain statistically accurate results

First principles study of sulfuric acid anion adsorption on a Pt(111) electrode

A first principles theory combined with a continuum electrolyte theory is applied to adsorption of sulfuric acid anions on Pt(111) in 0.1 M H2SO4 solution. The theoretical free energy diagram indicates that sulfuric acid anions adsorb as bisulfate in the potential range of 0.41 0.48 V (RHE) in good agreement with experiments reported in the literature. Vibration analysis indicates that the vibration frequencies observed experimentally at 1250 and 950 cm^[-1] can be assigned, respectively, to the S-O (uncoordinated) and symmetric S-O stretching modes for sulfate, and that the higher frequency mode has a larger potential-dependence (58 cm^[-1] V^[-1]) than the lower one

Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm

Catalytic activities are often dominated by a few specific surface sites, and designing active sites is the key to realize high-performance heterogeneous catalysts. The great triumphs of modern surface science lead to reproduce catalytic reaction rates by modeling the arrangement of surface atoms with well-defined single-crystal surfaces. However, this method has limitations in the case for highly inhomogeneous atomic configurations such as on alloy nanoparticles with atomic-scale defects, where the arrangement cannot be decomposed into single crystals. Here, we propose a universal machine-learning scheme using a local similarity kernel, which allows interrogation of catalytic activities based on local atomic configurations. We then apply it to direct NO decomposition on RhAu alloy nanoparticles. The proposed method can efficiently predict energetics of catalytic reactions on nanoparticles using DFT data on single crystals, and its combination with kinetic analysis can provide detailed information on structures of active sites and size- and composition-dependent catalytic activities

Translating insights from experimental analyses with single-crystal electrodes to practically-applicable material development strategies for controlling the Pt/ionomer interface in polymer electrolyte fuel cells

Ionomers are used in polymer electrolyte fuel cells (PEFCs) catalyst layers to improve proton conduction. Recent analytical studies have clarified that the adsorption of the ionomer on the surface of a Pt catalyst deteriorates the catalytic activity for the oxygen reduction reaction and oxygen transport properties near the catalyst surface. These findings have led to the development of new materials, such as mesoporous carbon support and highly oxygen-permeable ionomer, which are now commercially used. In this review article, we summarize recent analytical studies of the Pt/ionomer interface focusing on half-cell experiments with single-crystal electrodes. We also present promising approaches for mitigating ionomer adsorption, as well as the remaining challenges in the application of these approaches to PEFCs

A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks

Abstract Material informatics (MI) is a promising approach to liberate us from the time-consuming Edisonian (trial and error) process for material discoveries, driven by machine-learning algorithms. Several descriptors, which are encoded material features to feed computers, were proposed in the last few decades. Especially to solid systems, however, their insufficient representations of three dimensionality of field quantities such as electron distributions and local potentials have critically hindered broad and practical successes of the solid-state MI. We develop a simple, generic 3D voxel descriptor that compacts any field quantities, in such a suitable way to implement convolutional neural networks (CNNs). We examine the 3D voxel descriptor encoded from the electron distribution by a regression test with 680 oxides data. The present scheme outperforms other existing descriptors in the prediction of Hartree energies that are significantly relevant to the long-wavelength distribution of the valence electrons. The results indicate that this scheme can forecast any functionals of field quantities just by learning sufficient amount of data, if there is an explicit correlation between the target properties and field quantities. This 3D descriptor opens a way to import prominent CNNs-based algorithms of supervised, semi-supervised and reinforcement learnings into the solid-state MI