In the global trend of vigorously developing hydrogen energy, proton-conducting solid oxide electrolysis cells (P-SOECs) have attracted significant attention due to their advantages of high efficiency and not requiring precious metals. However, the application of P-SOECs faces challenges, particularly in developing high-performance anodes possessing both high catalytic activity and ionic conductivity. In this study, La_0.9Ba_0.1Co_0.7Ni_0.3O_3−δ (LBCN9173) and La_0.9Ca_0.1Co_0.7Ni_0.3O_3−δ (LCCN9173) oxides are tailored as promising anodes by machine learning model, achieving the synergistic enhancement of water oxidation reaction kinetics and proton conduction, which is confirmed by comprehensively analyzing experiment and density functional theory calculation results. Furthermore, the anodic reaction mechanisms for P-SOECs with these anodes are elucidated by analyzing distribution of relaxation time spectra and Gibbs energy of water oxidation reaction, manifesting that the dissociation of H₂O is facilitated on LBCN9173 anode. As a result, P-SOEC with LBCN9173 anode demonstrates a top-rank current density of 2.45 A cm⁻² at 1.3 V and an extremely low polarization resistance of 0.05 Ω cm² at 650 °C. This multi-scale, multi-faceted research approach not only discovered a high-performance anode but also proved the robust framework for the machine learning-assisted design of anodes for P-SOECs.

Powdery mildew poses a persistent threat to global melon production, compromising yield and fruit quality. 

Low temperatures significantly impair the growth and yield of pepper plants, a critical crop in global agriculture.

In cheminformatics, where machine learning is transforming our understanding of how molecular properties are predicted and explained, a critical challenge has long remained: making these powerful but often "black box" models easier to interpret. Recently, researchers at the Australian National University developed a breakthrough solution: a "regional explanation" method that helps reveal how molecular structures drive their properties. This research was published June 3 in Intelligent Computing, a Science Partner Journal, in an article titled “Regional Explanations and Diverse Molecular Representations in Cheminformatics: A Comparative Study.”

Understanding how nutrients are synthesized and distributed during seedling development is key to optimizing crop performance.

Why do some peaches turn red in the flesh, while others remain pale—even under the same conditions? 

The dazzling colors of lily petals are powered by anthocyanins, but how these pigments respond to light has long remained a mystery. 

A comprehensive survey published May 23 in Intelligent Computing, a Science Partner Journal maps out the role of large language models in task planning, underscoring the growing influence of artificial intelligence in complex decision-making tasks.

Understanding how environmental signals regulate flowering is crucial for crop improvement.