Machine learning-aided understanding of the structure-activity relationship: a case study of MoS supported metal-nonmetal pairs for the hydrogen evolution reaction.

Understanding the structure-performance relationship is crucial for designing highly active electrocatalysts, yet this remains a challenge. Using MoS supported metal-nonmetal atom pairs (XTM@MoS, TM = Sc-Ni, and X = B, C, N, O, P, Se, Te, and S) for the hydrogen evolution reaction (HER) as an example, we successfully uncovered the structure-activity relationship with the help of density functional theory (DFT) calculations and integrated machine learning (ML) methods. An ML model based on random forest regression was used to predict the activity, and the trained model exhibited excellent performance with minimal error.

[Sex ratio adjustment of a non-pollinating fig wasp species on Ficus semicordata in Xishuangbanna].

Through controlling the number of ovipositing foundresses inside a fig, and combining with the observation of ovipositing behavior and mating behavior, this paper studied the sex ratio of Apocryptophagus sp., a species of non-pollinating fig wasps hosted on Ficus semicordata in Xishuangbanna. The results showed that female Apocryptophagus sp.