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.

Electrochemical carbon dioxide reduction reaction (ECO2RR) is a promising approach to synthesize fuels and value-added chemical feedstocks while reducing atmospheric CO2 levels. Here, high surface area cerium and sulfur-doped hierarchical bismuth oxide nanosheets (Ce@S-Bi2O3) are develpoed by a solvothermal method. The resulting Ce@S-Bi2O3 electrocatalyst shows a maximum formate Faradaic efficiency (FE) of 92.5% and a current density of 42.09 mA cm−2 at −1.16 V versus RHE using a traditional H-cell system. Furthermore, using a three-chamber gas diffusion electrode (GDE) reactor, a maximum formate FE of 85% is achieved in a wide range of applied potentials (−0.86 to −1.36 V vs RHE) using Ce@S-Bi2O3. The density functional theory (DFT) results show that doping of Ce and S in Bi2O3 enhances formate production by weakening the OH and H species. Moreover, DFT calculations reveal that OCHO is a dominant pathway on Ce@S-Bi2O3 that leads to efficient formate production. This study opens up new avenues for designing metal and element-doped electrocatalysts to improve the catalytic activity and selectivity for ECO2RR.