Lithium metal batteries are a promising candidate for future high-energy-density energy storage. However, dendrite growth and the high reactivity of the Li metal anode result in low cycling efficiency and severe safety concerns. Here, we present a strategy to stabilize the lithium metal anode through cation doping in Li₇La₃Zr₂O₁₂ (LLZOM, M = dopant). High-throughput automated reaction screening together with a machine learning approach are developed to evaluate possible reactions and the thermodynamic stability of the Li|LLZOM interfaces under various chemical conditions. It is discovered that some dopants, such as M = Sc³⁺ (doping on Zr site), Ce³⁺ (La or Zr), Ac³⁺ (La), Y³⁺ (La or Zr), Tm³⁺ (La or Zr), Er³⁺ (La or Zr), Ho³⁺ (La or Zr), Dy³⁺ (La or Zr), Nd³⁺ (La or Zr), Tb³⁺ (La or Zr), Pr³⁺ (La), Pm³⁺ (La or Zr), Sm³⁺ (La or Zr), Gd³⁺ (La or Zr), Lu³⁺ (La), Ce⁴⁺ (Zr), Th⁴⁺ (Zr), and Pa⁵⁺ (Zr), exhibit thermodynamic stability against Li; while others, M = Ca²⁺ (La or Zr), Yb³⁺ (La), Br³⁺ (Li), Te⁴⁺ (Zr), Se⁴⁺ (Zr), S⁴⁺ (Zr), Hf⁴⁺ (Zr), Cl⁵⁺ (Zr), and I⁵⁺ (Zr), may lead to the spontaneous formation of a stable, passivating solid electrolyte interphase (SEI) layer on the Li metal, and alleviate dendritic lithium growth. From the machine learning approach, the formation energy of oxides M_xO_y emerges as the most crucial feature dominating the route of interface reactions, implying that the M–O bond strength governs the interface stability of the cation-doped LLZOM. The machine learning model then predicts 18 unexplored LLZOM systems, which are all validated in subsequent calculations. Our work offers practical insights for experimentalists into the selection of appropriate dopants in LLZO to stabilize Li metal anodes in solid-state batteries.

Transition metal oxides are promising electrocatalysts for zinc-air batteries, yet surface reconstruction caused by the adsorbate evolution mechanism, which induces zinc-ion battery behavior in the oxygen evolution reaction, leads to poor cycling performance. In this study, we propose a lattice oxygen mechanism involving proton acceptors to overcome the poor performance of the battery in the OER process. We introduce a stable solid base, hydroxy BaCaSiO₄, onto the surfaces of PrBa_0.5Ca_0.5Co₂O_5+δ perovskite nanofibers with a one-step exsolution strategy. The HO-Si sites on the hydroxy BaCaSiO₄ significantly accelerate proton transfer from the OH* adsorbed on PrBa_0.5Ca_0.5Co₂O_5+δ during the OER process. As a proof of concept, a rechargeable zinc-air battery assembled with this composite electrocatalyst is stable in an alkaline environment for over 150 hours at 5 mA cm^–2 during galvanostatic charge/discharge tests. Our findings open new avenues for designing efficient OER electrocatalysts for rechargeable zinc-air batteries.

It is well believed that machine learning models could help to predict the formation energies of materials if all elemental and crystal structural details are known. In this paper, it is shown that even without detailed crystal structure information, the formation energies of binary compounds in various prototypes at the ground states can be reasonably evaluated using machine-learning feature abstraction to screen out the important features. By combining with the “white-box” sure independence screening and sparsifying operator (SISSO) approach, an interpretable and accurate formation energy model is constructed. The predicted formation energies of 183 experimental and 439 calculated stable binary compounds (E_hull = 0) are predicted using this model, and both show reasonable agreements with experimental and Materials Project’s calculated values. The descriptor set is capable of reflecting the formation energies of binary compounds and is also consistent with the common understanding that the formation energy is mainly determined by electronegativity, electron affinity, bond energy, and other atomic properties. As crystal structure parameters are not necessary prerequisites, it can be widely applied to the formation energy prediction and classification of binary compounds in large quantities.