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Abstract A machine learning (ML) guided approach is presented for the accelerated optimization of chemical vapor deposition (CVD) synthesis of 2D materials toward the highest quality, starting from low‐quality or unsuccessful synthesis conditions. Using 26 sets of these synthesis conditions as the initial training dataset, our method systematically guides experimental synthesis towards optoelectronic‐grade monolayer MoS₂flakes. A‐exciton linewidth (σ_A) as narrow as 38 meV could be achieved in 2D MoS₂flakes after only an additional 35 trials (reflecting 15% of the full factorial design dataset for training purposes). In practical terms, this reflects a decrease of the possible experimental time to optimize the parameters from up to one year to about two months. This remarkable efficiency was achieved by formulating a constrained sequencing optimization problem solved via a combination of constraint learning and Bayesian Optimization with the narrowness of σ_Aas the single target metric. By employing graph‐based semi‐supervised learning with data acquired through a multi‐criteria sampling method, the constraint model effectively delineates and refines the feasible design space for monolayer flake production. Additionally, the Gaussian Process regression effectively captures the relationships between synthesis parameters and outcomes, offering high predictive capability along with a measure of prediction uncertainty. This method is scalable to a higher number of synthesis parameters and target metrics and is transferrable to other materials and types of reactors. This study envisions that this method will be fundamental for CVD and similar techniques in the future. 

Abstract Iron‐nitrogen‐carbon (Fe‐N‐C) single‐atom catalysts are promising sustainable alternatives to the costly and scarce platinum (Pt) to catalyze the oxygen reduction reactions (ORR) at the cathode of proton exchange membrane fuel cells (PEMFCs). However, Fe‐N‐C cathodes for PEMFC are made thicker than Pt/C ones, in order to compensate for the lower intrinsic ORR activity and site density of Fe‐N‐C materials. The thick electrodes are bound with mass transport issues that limit their performance at high current densities, especially in H₂/air PEMFCs. Practical Fe‐N‐C electrodes must combine high intrinsic ORR activity, high site density, and fast mass transport. Herein, it has achieved an improved combination of these properties with a Fe‐N‐C catalyst prepared via a two‐step synthesis approach, constructing first a porous zinc‐nitrogen‐carbon (Zn‐N‐C) substrate, followed by transmetallating Zn by Fe via chemical vapor deposition. A cathode comprising this Fe‐N‐C catalyst has exhibited a maximum power density of 0.53 W cm⁻²in H₂/air PEMFC at 80 °C. The improved power density is associated with the hierarchical porosity of the Zn‐N‐C substrate of this work, which is achieved by epitaxial growth of ZIF‐8 onto g‐C₃N₄, leading to a micro‐mesoporous substrate.