Recalibrating the Experimentally Derived Structure of the Metastable Surface Oxide on Copper via Machine Learning-Accelerated In Silico Global Optimization.

The oxidation of copper and its surface oxides are gaining increasing attention due to the enhanced CO2 reduction reaction (CO2RR) activity exhibited by partially oxidized copper among the copper-based catalysts. The "8" surface oxide on Cu(111) is seen as a promising structure for further study due to its resemblance to the highly active Cu2O(110) surface in the C-C coupling of the CO2RR, setting it apart from other O/Cu(111) surface oxides resembling Cu2O(111). However, recent X-ray photoelectron spectroscopy analysis challenges the currently accepted atomic structure of the "8" surface oxide, prompting a need for reevaluation. This study highlights the limitations of conventional methods when addressing such challenges, leading us to adopt global optimization search techniques. After a rigorous process to ensure robustness, the unbiased global minimum of the "8" surface oxide is identified. Interestingly, this configuration differs significantly from other surface oxides and also from previous "8" models while retaining similarities to the Cu2O(110) surface.

Using Feature-Assisted Machine Learning Algorithms to Boost Polarity in Lead-Free Multicomponent Niobate Alloys for High-Performance Ferroelectrics.

To expand the unchartered materials space of lead-free ferroelectrics for smart digital technologies, tuning their compositional complexity via multicomponent alloying allows access to enhanced polar properties. The role of isovalent A-site in binary potassium niobate alloys, (K,A)NbO3 using first-principles calculations is investigated. Specifically, various alloy compositions of (K,A)NbO3 are considered and their mixing thermodynamics and associated polar properties are examined. To establish structure-property design rules for high-performance ferroelectrics, the sure independence screening sparsifying operator (SISSO) method is employed to extract key features to explain the A-site driven polarization in (K,A)NbO3 . Using a new metric of agreement via feature-assisted regression and classification, the SISSO model is further extended to predict A-site driven polarization in multicomponent systems as a function of alloy composition, reducing the prediction errors to less than 1%. With the machine learning model outlined in this work, a polarity-composition map is established to aid the development of new multicomponent lead-free polar oxides which can offer up to 25% boosting in A-site driven polarization and achieving more than 150% of the total polarization in pristine KNbO3 . This study offers a design-based rational route to develop lead-free multicomponent ferroelectric oxides for niche information technologies.