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Artificial Intelligence (AI) techniques such as Generative Neural Networks (GNNs) have resulted in remarkable breakthroughs such as the generation of hyper-realistic images, 3D geometries, and textual data. This work investigates the ability of STEM learners and educators to decipher AI generated video in order to safeguard the public-availability of high-quality online STEM learning content. The COVID-19 pandemic has increased STEM learners’ reliance on online learning content. Consequently, safeguarding the veracity of STEM learning content is critical to ensuring the safety and trust that both STEM educators and learners have in publicly-available STEM learning content. In this study, state of the art AI algorithms are trained on a specific STEM context (e.g., climate change) using publicly-available data. STEM learners are then presented with AI-generated STEM learning content and asked to determine whether the AI-generated output is visually convincing (i.e., “looks real”) and whether the context being presented is plausible. Knowledge gained from this study will help enhance society’s understanding of AI algorithms, their ability to generate convincing video output, and the threat that those generated output have in potentially deceiving STEM learners who may be exposed to them during online learning activities. 

Correction for ‘Optimizing accuracy and efficacy in data-driven materials discovery for the solar production of hydrogen’ by Yihuang Xionget al.,Energy Environ. Sci., 2021,14, 2335–2348; DOI: 10.1039/D0EE02984J. 

The production of hydrogen fuels, via water splitting, is of practical relevance for meeting global energy needs and mitigating the environmental consequences of fossil-fuel-based transportation. Water photoelectrolysis has been proposed as a viable approach for generating hydrogen, provided that stable and inexpensive photocatalysts with conversion efficiencies over 10% can be discovered, synthesized at scale, and successfully deployed (Pinaud et al. , Energy Environ. Sci. , 2013, 6 , 1983). While a number of first-principles studies have focused on the data-driven discovery of photocatalysts, in the absence of systematic experimental validation, the success rate of these predictions may be limited. We address this problem by developing a screening procedure with co-validation between experiment and theory to expedite the synthesis, characterization, and testing of the computationally predicted, most desirable materials. Starting with 70 150 compounds in the Materials Project database, the proposed protocol yielded 71 candidate photocatalysts, 11 of which were synthesized as single-phase materials. Experiments confirmed hydrogen generation and favorable band alignment for 6 of the 11 compounds, with the most promising ones belonging to the families of alkali and alkaline-earth indates and orthoplumbates. This study shows the accuracy of a nonempirical, Hubbard-corrected density-functional theory method to predict band gaps and band offsets at a fraction of the computational cost of hybrid functionals, and outlines an effective strategy to identify photocatalysts for solar hydrogen generation.