As a new type of inorganic ductile semiconductor, silver sulfide (α-Ag₂S) has garnered a plethora of interests in recent years due to its promising applications in flexible electronics. However, the lack of detailed defect calculations and chemical intuition has largely hindered the optimization of material's performance. In this study, we systematically investigate the defect chemistry of extrinsic doping in α-Ag₂S using first-principles calculations. We computationally examine a broad suite of 17 dopants and find that all aliovalent elements have extremely low doping limits (<0.002%) in α-Ag₂S, rendering them ineffective in tuning the electron concentrations. In contrast, the isovalent elements Se and Te have relatively high doping limits, being consistent with the experimental observations. While the dopant Se or Te itself does not provide additional electrons, its introduction has a significant impact on the band gap, the band-edge position, and especially the formation energy of Ag interstitials, which effectively improve the electron concentrations by 2–3 orders of magnitudes. The size effects of Se and Te doping are responsible for the more favorable Ag interstitials in Ag₂S_0.875Se_0.125 and Ag₂S_0.875Te_0.125 with respect to pristine Ag₂S. This work serves as a theoretical foundation for the rational design of Ag₂S-based functional materials.

Magnesium (Mg) alloys have shown great prospects as both structural and biomedical materials, while poor corrosion resistance limits their further application. In this work, to avoid the time-consuming and laborious experiment trial, a high-throughput computational strategy based on first-principles calculations is designed for screening corrosion-resistant binary Mg alloy with intermetallics, from both the thermodynamic and kinetic perspectives. The stable binary Mg intermetallics with low equilibrium potential difference with respect to the Mg matrix are firstly identified. Then, the hydrogen adsorption energies on the surfaces of these Mg intermetallics are calculated, and the corrosion exchange current density is further calculated by a hydrogen evolution reaction (HER) kinetic model. Several intermetallics, e.g. Y₃Mg, Y₂Mg and La₅Mg, are identified to be promising intermetallics which might effectively hinder the cathodic HER. Furthermore, machine learning (ML) models are developed to predict Mg intermetallics with proper hydrogen adsorption energy employing work function (W_f) and weighted first ionization energy (WFIE). The generalization of the ML models is tested on five new binary Mg intermetallics with the average root mean square error (RMSE) of 0.11 eV. This study not only predicts some promising binary Mg intermetallics which may suppress the galvanic corrosion, but also provides a high-throughput screening strategy and ML models for the design of corrosion-resistant alloy, which can be extended to ternary Mg alloys or other alloy systems.