The Haber-Bosch (HB) process combining nitrogen (N 2 ) and hydrogen (H 2 ) into ammonia (NH 3 ) gases plays an essential role in the synthesis of fertilizers for food production and many other commodities. However, HB requires enormous energy resources (2% of world energy production) and the high pressures and temperatures make NH 3 production facilities very expensive. Recent advances in improving HB catalysts have been incremental and slow. To accelerate the development of improved HB catalysts, we developed a hierarchical highthroughput catalyst screening (HHTCS) approach based on the recently developed complete reaction mechanism to identify non-transition metal (NTM) elements from a total set of 18 candidates that can significantly improve the efficiency of the most active Fe surface, Febcc(111), through surface and subsurface doping. Surprisingly, we found a very promising subsurface dopant, Si, that had not been identified nor suggested previously, showing the importance of the subsurface Fe atoms in N 2 reduction reactions. Then we derived the full reaction path of the HB process for the Si doped Fe-bcc(111) from QM simulations which we combined with kinetic Monte Carlo (kMC) simulations to predict a ~13-fold increase in turnover frequency (TOF) under typical extreme HB conditions (200 atm reactant pressure and 500 °C) and a ~43-fold increase in TOF under ideal HB conditions (20 atm reactant pressure and 400 °C) for the Si-doped Fe catalyst, compared to pure Fe catalyst. Importantly, the Si doped Fe catalyst can achieve the same TOF of pure Fe at 200 atm/500 °C under much milder conditions: e.g., at a much decreased reactant pressure of 20 atm at 500 °C, or alternatively at temperature and reactant pressure decreased to 400 °C and 50 atm, respectively. Production plants using the new catalysts that operate under such milder conditions could be much less expensive, allowing production at local sites needing fertilizer.

To discover more efficient industrial catalysts for ammonia synthesis via the Haber-Bosch (HB) process, we employed quantum-mechanics (QM)-based hierarchical high throughput catalyst screening (HHTCS) to test a wide group of elements (34) as candidates to dope the Fe(111) catalyst subsurface. The QM freeenergy reaction network of HB over Fe(111) yields ten barriers as potentially rate-determining, of which we select four as prototypical, arrange them hierarchically, and define a corresponding set of screening criteria, which we then use to screen candidate catalysts. This leads to two promising candidates (Co and Ni), from which we selected the most promising (Ni) for a complete QM and kinetic study. The kinetic Monte Carlo (kMC) simulations predict a 16-fold increase in HB turn over frequency (TOF) for the Nidoped catalyst compared to the pure Fe(111) surface under realistic conditions. The 16-fold increase in HB turn over frequency (TOF) is a significant improvement and may trigger future experimental studies to validate our prediction. This TOF improvement could lead to similar reaction rates as with pure Fe but at areaction temperature decreased by 100 degrees from 773 to 673 K and a total reactant pressure decreased by 6 times from 201 atm to 34 atm. We interpret the reasons underlying this improvement using Valence Bond and kinetic analyses. We suggest this Ni-doped Fe(111) catalyst as a candidate to reduce the world energy consumption for the HB process while satisfying future needs for energy and environment.

We showed recently that the catalytic efficiency of ammonia synthesis on Fe-based nanoparticles (NP) for Haber–Bosch (HB) reduction of N2 to ammonia depends very dramatically on the crystal surface exposed and on the doping. In turn, the stability of each surface depends on the stable intermediates present during the catalysis. Thus, under reaction conditions, the shape of the NP is expected to evolve to optimize surface energies. In this paper, we propose to manipulate the shape of the nanoparticles through doping combined with chemisorption and catalysis. To do this, we consider the relationships between the catalyst composition (adding dopant elements) and on how the distribution of the dopant atoms on the bulk and facet sites affects the shape of the particles and therefore the number of active sites on the catalyst surfaces. We use our hierarchical, high-throughput catalyst screening (HHTCS) approach but extend the scope of HHTCS to select dopants that can increase the catalytically active surface orientations, such as Fe-bcc(111), at the expense of catalytically inactive facets, such as Fe-bcc(100). Then, for the most promising dopants, we predict the resulting shape and activity of doped Fe-based nanoparticles under reaction conditions. We examined 34 possible dopants across the periodic table and found 16 dopants that can potentially increase the fraction of active Fe-bcc(111) vs inactive Fe-bcc(100) facets. Combining this reshaping criterion with our HHTCS estimate of the resulting catalytic performance, we show that Si and Ni are the most promising elements for improving the rates of catalysis by optimizing the shape to decrease reaction barriers. Then, using Si dopant as a working example, we build a steady-state dynamical Wulff construction of Si-doped Fe bcc nanoparticles. We use nanoparticles with a diameter of ∼10 nm, typical of industrial catalysts. We predict that doping Si into such Fe nanoparticles at the optimal atomic content of ∼0.3% leads to rate enhancements by a factor of 56 per nanoparticle under target HB conditions.