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.

Synthesis of new inorganic phases relies on expert intuitions, laborious syntheses, and serendipity. Here, we propose a data-driven model based on positive-unlabeled learning to guide synthesis experiments by predicting, for any given elemental stoichiometries, the likelihood of synthesizing inorganic materials. Our synthesizability prediction model shows a true positive rate of 83.4% for the test dataset and an estimated precision of 83.6%. The ability of our model to treat arbitrary elemental combinations allows one to construct the continuous synthesizability phase map in good agreement with the available synthetic data. Furthermore, we use our model to guide experimental exploration of the quaternary oxide compositional space comprising CuO, Fe2O3, and V2O5, resulting in the discovery of a new phase, Cu4FeV3O13. We expect that our approach could aid the synthesis of new inorganic compositions by suggesting synthetically more accessible stoichiometries.

Oxygen evolution reaction (OER) can convert renewable energy into hydrogen through water electrolysis. Identifying stable and active single-atom catalysts (SACs) for OER under acidic conditions holds great promise for developing cost-effective and efficient energy storage solutions, but challenging due to the vast number of potential material compositions and diverse surface morphologies. Here, to accelerate new discoveries, we present a high-throughput screening (HTS) framework that leverages the power of machine learning (ML) and density functional theory (DFT). The proposed framework includes an assessment of both the thermodynamic and electrochemical stability of support surfaces. In addition, the integration of ML and uncertainty quantification for predicting the binding energies dramatically reduces the computational cost (by over a factor of 10), facilitating the identification of catalytically active SACs. Following the proposed scheme, we suggest 14 new promising SACs for OER across the 795 binary oxide supports and 21 transition metal atom combinations. These catalysts are found to break the scaling relation due to the enhanced *OOH binding with the support, which arises from favorable hydrogen bonding interactions.