NH3 fuel-based SOFCs can overcome the inefficiencies of other hydrogen sources, but NH3 deteriorates solid oxide fuel cell (SOFC) separators by nitriding. Here, experimental and computational approaches are used to investigate aluminum-doped zinc oxide (AZO) as a SOFC separator coating candidate for nitriding mitigation. Experimental characterization reveals how AZO coating slows down the nitriding of steel separators, and that AZO conductivity improves upon NH3 exposure. A phase diagram construction framework combines a machine learning potential (MLP), density functional theory (DFT), and grand potential energy calculations to confirm the phase stability of AZO-based coatings under operating conditions. An a priori linear response simulation approach is developed to precisely reproduce ZnO electronic structure and conductivity. This methodology is adapted to explain why AZO-based coating color changes upon nitriding, the physical contributions to which are determined by AZO conductivity versus Al impurity concentration relationships, and explain how steam incorporation into AZO-based coatings can increase their conductivity. Energy-dispersive x-ray Spectroscopy (EDS), and the calculation of NHx adsorption energetics, infers ZnO surface nitriding susceptibility diverts nitrides away from other coating or separator components. This work demonstrates insights into the design of nitriding-resistant materials for NH3-based SOFCs.

The development of accurate methods for determining how alloy surfaces spontaneously restructure under reactive and corrosive environments is a key, long-standing, grand challenge in materials science. Using machine learning-accelerated density functional theory and rare-event methods, in conjunction with in situ environmental transmission electron microscopy (ETEM), we examine the interplay between surface reconstructions and preferential segregation tendencies of CuNi(100) surfaces under oxidation conditions. Our modeling approach predicts that oxygen-induced Ni segregation in CuNi alloys favors Cu(100)-O c(2 × 2) reconstruction and destabilizes the Cu(100)-O (2√2 × √2)R45° missing row reconstruction (MRR). In situ ETEM experiments validate these predictions and show Ni segregation followed by NiO nucleation and growth in regions without MRR, with secondary nucleation and growth of Cu2O in MRR regions. Our approach based on combining disparate computational components and in situ ETEM provides a holistic description of the oxidation mechanism in CuNi, which applies to other alloy systems.

Modeling adsorbates on single-crystal metals is critical in rational catalyst design and other research that requires detailed thermochemistry. First-principles simulations via density functional theory (DFT) are among the prevalent tools to acquire such information about surface species. While they are highly dependable, DFT calculations often require intensive computational resources and runtime. These limiting factors become particularly pronounced when investigating large sets of complex molecules on heavy noble metals. Consequently, our ability to explore these species and their corresponding energetics is limited. In this work, we establish a novel framework that utilizes techniques including molecular encoding, descriptor synthesis, and machine learning to overcome the limitation of costly DFT simulations. Simultaneously, we estimate thermochemical information efficiently at the DFT accuracy level. More specifically, we translated our training molecules into text-based identifiers through a simplified molecular-input line-entry system. Following that, we parametrize our training matrices with sets of short-range descriptors based on group methods, applying first the nearest neighbors to account for linear contributions. This is coupled with the long-range descriptors characterizing second nearest neighbors to account for nonlinear corrections. Finally, we use linear regression and machine learning techniques, such as Gaussian process regressions to regress over the linear and nonlinear matrix systems, respectively. This is the first work to our knowledge that encompasses both the first and second nearest neighbors based on the group theory throughout the featurization, training, and deployment stages. We trained and validated our models with 459 surface species on Pt(111), Ru(0001), and Ir(111) surfaces. Results exhibit robust performance to reproduce the energetics of interest, such as enthalpies, entropies, and heat capacities, at various temperatures. Notably, the mean absolute errors can be reduced by 48% during training and 19% during prediction at a minimum, when compared to the classical group method. Leveraging the novel framework, our machine-learning-enabled thermochemistry estimator significantly empowers us to research the thermochemistry of complex species on metal catalysts.