Artificial intelligence (AI) has received widespread attention over the last few decades due to its potential to increase automation and accelerate productivity. In recent years, a large number of training data, improved computing power, and advanced deep learning algorithms are conducive to the wide application of AI, including material research. The traditional trial‐and‐error method is inefficient and time‐consuming to study materials. Therefore, AI, especially machine learning, can accelerate the process by learning rules from datasets and building models to predict. This is completely different from computational chemistry where a computer is only a calculator, using hard‐coded formulas provided by human experts. Herein, the application of AI in material innovation is reviewed, including material design, performance prediction, and synthesis. The realization details of AI techniques and advantages over conventional methods are emphasized in these applications. Finally, the future development direction of AI is expounded from both algorithm and infrastructure aspects.

The modulation effect manifests an encouraging potential to enhance the performance of single‐atom catalysts; however, the in‐depth study about this effect for the isolated diatomic sites (DASs) remains a great challenge. Herein, a proximity electronic effect (PEE) of Ni/Co DASs is proposed that is anchored in N‐doped carbon (N‐C) substrate (NiCo DASs/N‐C) for synergistic promoting electrocatalytic oxygen reduction reaction (ORR) and hydrogen evolution reaction (HER). Benefiting from the PEE of adjacent Ni anchored by four nitrogen (Ni‐N4) moiety, NiCo DASs/N‐C catalyst exhibits superior ORR and HER activity. In situ characterization results suggest Co anchored by four nitrogen (Co‐N4) as main active site for O2 adsorption‐activation process, which promotes the formation of key *OOH and the desorption of *OH intermediate to accelerate the multielectron reaction kinetics. Theoretical calculation reveals the adjacent Ni‐N4 site as a modulator can effectively adjust the electronic localization of proximity Co‐N4 site, promoting the *OH desorption and *H adsorption on Co‐N4 site, thereby boosting ORR and HER process significantly. This study opens a new opportunity for rationally regulating the electronic localization of catalytic active centers by proximity single‐atom moiety, as well as provides guidance for designing high‐efficiency bifunctional electrocatalysts for promising applications.

Understanding how tourists’ brand experiences impact their existential authenticity, and the role of existential authenticity in the formation mechanism of place attachment to the destination, are key issues for the marketing of a destination. The current study examines the relationship between tourists’ experience, existential authenticity, and place attachment, and the indirect effect of existential authenticity on the relationship between destination brand experience and place attachment from the oriental perspective against the slow tourism background. A self-administered survey was conducted at Yaxi town, the first international slow city in China. A total of 398 samples were analyzed using a two-step approach of the structural equation model (SEM). The findings show that destination brand experience partially impacts existential authenticity, and both the intrapersonal and interpersonal authenticity (the sub-dimensions of existential authenticity) significantly influence place attachment. Additionally, affective and behavioral experience indirectly influence place attachment through existential authenticity. Based on the conclusions, theoretical and practical recommendations are considered.

An in-depth discussion of place attachment in the relationship between residents’ perceived tourism impacts and their support for tourism is still lacking. The predictor of tourism involvement in this relationship has also been underestimated and little attention has been paid to industrial heritage tourism in relation to residents’ attitudes toward tourism development. To fill this gap, we extend upon the existing studies of residents’ attitudes toward tourism with place attachment (both place identity and place dependence) and tourism involvement based on social exchange theory, attitude theory, and the theory of planned behavior. A self-administered survey was completed by 336 residents of Huangshi, a city undergoing a transition to industrial heritage tourism in China. The findings show that residents’ support for tourism is the result of a complete behavior generation process. This has gradually formed through tourism involvement, cognition, affection, and behavior intention, emphasizing the importance of participation and affective attitude in determining residents’ attitudes toward tourism. To maintain the sustainable development of industrial heritage tourism in the economic transition from an old industrial region to new sectors, local authorities should attach more importance to strengthening residents’ native emotional bonds and concentrate on how to encourage local residents to participate in tourism activities.