The sunlight-driven reduction of CO₂ into fuels and platform chemicals is a promising approach to enable a circular economy. However, established optimization approaches are poorly suited to multivariable multimetric photocatalytic systems because they aim to optimize one performance metric while sacrificing the others and thereby limit overall system performance. Herein, we address this multimetric challenge by defining a metric for holistic system performance that takes multiple figures of merit into account, and employ a machine learning algorithm to efficiently guide our experiments through the large parameter matrix to make holistic optimization accessible for human experimentalists. As a test platform, we employ a five-component system that self-assembles into photocatalytic micelles for CO₂-to-CO reduction, which we experimentally optimized to simultaneously improve yield, quantum yield, turnover number, and frequency while maintaining high selectivity. Leveraging the data set with machine learning algorithms allows quantification of each parameter's effect on overall system performance. The buffer concentration is unexpectedly revealed as the dominating parameter for optimal photocatalytic activity, and is nearly four times more important than the catalyst concentration. The expanded use and standardization of this methodology to define and optimize holistic performance will accelerate progress in different areas of catalysis by providing unprecedented insights into performance bottlenecks, enhancing comparability, and taking results beyond comparison of subjective figures of merit.

We focus on gas sorption within metal-organic frameworks (MOFs) for energy applications and identify the minimal set of crystallographic descriptors underpinning the most important properties of MOFs for CO₂ and H₂O. A comprehensive comparison of several sequential learning algorithms for MOFs properties optimization is performed and the role played by those descriptors is clarified. In energy transformations, thermodynamic limits of important figures of merit crucially depend on equilibrium properties in a wide range of sorbate coverage values, which is often only partially accessible, hence possibly preventing the computation of desired objective functions. We propose a fast procedure for optimizing specific energy in a closed sorption energy storage system with only access to a single water Henry coefficient value and to the specific surface area. We are thus able to identify hypothetical candidate MOFs that are predicted to outperform state-of-the-art water-sorbent pairs for thermal energy storage applications.

We perform a high-throughput computational search for novel phonon-mediated superconductors, starting from the Materials Cloud three-dimensional structure database of experimentally known inorganic stoichiometric compounds. We first compute the Allen-Dynes critical temperature ( T c AD ) for 4533 nonmagnetic metals using a direct and progressively finer sampling of the electron-phonon couplings. For the candidates with the largest T c AD value, we use automated Wannierizations and electron-phonon interpolations to obtain a high-quality data set for the most promising 250 dynamically stable structures, for which we calculate spectral functions, superconducting band gaps, and isotropic Migdal-Eliashberg critical temperatures. For 140 of these, we also provide anisotropic Migdal-Eliashberg superconducting gaps and critical temperatures. The approach is remarkably successful in finding known superconductors and we find 24 unknown ones with a predicted anisotropic T c value above 10 K. Among them, we identify a possible double-gap superconductor ( p -doped Ba B 2 ), a nonmagnetic half-Heusler Zr Ru Sb , and the perovskite Ta Ru 3 C , all exhibiting significant T c values. Finally, we introduce a sensitivity analysis to estimate the robustness of the predictions.