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The solar–to–chemical energy conversion of Earth-abundant resources like water or greenhouse gas pollutants like CO₂promises an alternate energy source that is clean, renewable, and environmentally friendly. The eventual large-scale application of such photo-based energy conversion devices can be realized through the discovery of novel photocatalytic materials that are efficient, selective, and robust. In the past decade, the Materials Genome Initiative has led to a major leap in the development of materials databases, both computational and experimental. Hundreds of photocatalysts have recently been discovered for various chemical reactions, such as water splitting and carbon dioxide reduction, employing these databases and/or data informatics, machine learning, and high-throughput computational and experimental methods. In this article, we review these data-driven photocatalyst discoveries, emphasizing the methods and techniques developed in the last few years to determine the (photo)electrochemical stability of photocatalysts, leading to the discovery of photocatalysts that remain robust and durable under operational conditions. 

Abstract The formation and disassociation of excitons play a crucial role in any photovoltaic or photocatalytic application. However, excitonic effects are seldom considered in materials discovery studies due to the monumental computational cost associated with the examination of these properties. Here, we study the excitonic properties of nearly 50 photocatalysts using state-of-the-art Bethe–Salpeter formalism. These ~50 materials were recently recognized as promising photocatalysts for CO₂reduction through a data-driven screening of 68,860 materials. Here, we propose three screening criteria based on the optical properties of these materials, taking excitonic effects into account, to further down select six materials. Furthermore, we study the correlation between the exciton binding energies obtained from the Bethe–Salpeter formalism and those obtained from the computationally much less-expensive Wannier–Mott model for these chemically diverse ~50 materials. This work presents a paradigm towards the inclusion of excitonic effects in future materials discovery for solar-energy harvesting applications. 

Insights from organisms, which have evolved natural strategies for promoting survivability under extreme environmental pressures, may help guide future research into novel approaches for enhancing human longevity. The cave-adapted Mexican tetra, Astyanax mexicanus , has attracted interest as a model system for metabolic resilience , a term we use to denote the property of maintaining health and longevity under conditions that would be highly deleterious in other organisms (Figure 1). Cave-dwelling populations of Mexican tetra exhibit elevated blood glucose, insulin resistance and hypertrophic visceral adipocytes compared to surface-dwelling counterparts. However, cavefish appear to avoid pathologies typically associated with these conditions, such as accumulation of advanced-glycation-end-products (AGEs) and chronic tissue inflammation. The metabolic strategies underlying the resilience properties of A. mexicanus cavefish, and how they relate to environmental challenges of the cave environment, are poorly understood. Here, we provide an untargeted metabolomics study of long- and short-term fasting in two A. mexicanus cave populations and one surface population. We find that, although the metabolome of cavefish bears many similarities with pathological conditions such as metabolic syndrome, cavefish also exhibit features not commonly associated with a pathological condition, and in some cases considered indicative of an overall robust metabolic condition. These include a reduction in cholesteryl esters and intermediates of protein glycation, and an increase in antioxidants and metabolites associated with hypoxia and longevity. This work suggests that certain metabolic features associated with human pathologies are either not intrinsically harmful, or can be counteracted by reciprocal adaptations. We provide a transparent pipeline for reproducing our analysis and a Shiny app for other researchers to explore and visualize our dataset. 

Abstract Photoelectrocatalysts that use sunlight to power the CO 2 reduction reaction will be crucial for carbon-neutral power and energy-efficient industrial processes. Scalable photoelectrocatalysts must satisfy a stringent set of criteria, such as stability under operating conditions, product selectivity, and efficient light absorption. Two-dimensional materials can offer high specific surface area, tunability, and potential for heterostructuring, providing a fresh landscape of candidate catalysts. From a set of promising bulk CO 2 reduction photoelectrocatalysts, we screen for candidate monolayers of these materials, then study their catalytic feasibility and suitability. For stable monolayer candidates, we verify the presence of visible-light band gaps, check that band edges can support CO 2 reduction, determine exciton binding energies, and compute surface reactivity. We find visible light absorption for SiAs, ZnTe, and ZnSe monolayers, and that due to a lack of binding, CO selectivity is possible. We thus identify SiAs, ZnTe, and ZnSe monolayers as targets for further investigation, expanding the chemical space for CO 2 photoreduction candidates.