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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. 

We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form$$\hbox {A}_2\hbox {B}_2\hbox {X}_6$$${\text{A}}_2{\text{B}}_2{\text{X}}_6$, based on the known material$$\hbox {Cr}_2\hbox {Ge}_2\hbox {Te}_6$$${\text{Cr}}_2{\text{Ge}}_2{\text{Te}}_6$, using density functional theory (DFT) calculations and machine learning methods to determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability. We show that machine learning tools, combined with DFT calculations, can provide a computationally efficient means to predict properties of such two-dimensional (2D) magnetic materials. Our data analytics approach provides insights into the microscopic origins of magnetic ordering in these systems. For instance, we find that the X site strongly affects the magnetic coupling between neighboring A sites, which drives the magnetic ordering. Our approach opens new ways for rapid discovery of chemically stable vdW materials that exhibit magnetic behavior.