Sieving Hydrogen Isotopes via Machine Learning Assisted Chemical Vapor Deposition (CVD) of High‐Quality Monolayer Hexagonal Boron Nitride (h‐BN) on Iron Foils

Abstract Atomically thin two‐dimensional (2D) ceramics, such as monolayer hexagonal boron nitride (h‐BN), present potential for disruptive advances in separations. However, sub‐atomic scale separation of hydrogen isotopes (H + /D + ) require near pristine 2D material membranes, and scalable synthesis of such high‐quality h‐BN comparable to mechanically exfoliated crystals remains a significant challenge. Here, we report a scalable Fe‐catalyzed chemical vapor deposition (CVD) process for bottom‐up synthesis of large‐area, high‐quality monolayer h‐BN films, overcoming key limitations of conventional ammonia‐based routes. By leveraging mechanistic insights and higher CVD temperatures, we suppress multilayer formation and achieve uniform monolayer h‐BN coverage on commercially available Fe foils. Machine learning enables systematic exploration of the complex, multi‐dimensional CVD parameter space (growth time, temperature, precursor temperature, multilayer faction, coverage), providing data‐driven approaches to visualize and identify process regimes facilitating predominantly monolayer h‐BN growth with minimal secondary nuclei/ad‐layers. The optimized Fe‐catalyzed CVD h ‐BN membranes show high‐quality as observed by proton/deuteron (H + /D + ) selectivity ≈8.45, approaching the highest quality benchmark of mechanically exfoliated h‐BN (H + /D + selectivity ≈10) as well as significantly outperforming Cu‐catalyzed CVD h ‐BN membranes (H + /D + selectivity ≈3.62, control selectivity ≈1.7). Our work provides a scalable cost‐effective route for high‐quality monolayer h‐BN synthesis for sub‐atomic scale separations (H + /D + ) and demonstrates the broader potential of machine learning‐guided optimization of CVD for advancing synthesis of 2D materials.