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.

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.