Bayesian Optimization of Carbon Nanolattices Machine Learning designs new nanolattice geometries with the strength of carbon steel, but the density of Styrofoam, offering record strength-to-weight of lightweight materials. By implementing multi-objective Bayesian optimization in combination with two-photon polymerization and pyrolysis, these ultrahigh specific strength carbon nanolattices more than double the performance of benchmark materials. More details can be found in article number 2410651 by Peter Serles, Tobin Filleter, Seunghwa Ryu, and co-workers.