Neural Network Sampling of the Free Energy Landscape for Nitrogen Dissociation on Ruthenium
Year: 2021
Type: article
Abstract: In heterogeneous catalysis, free energy profiles of reactions govern the mechanisms, rates, and equilibria. Energetics are conventionally computed using the harmonic approximation (HA), which requires determination of critical states a priori. Here, we use neural networks to efficiently sample and directly calculate the free energy surface (FES) of a prototypical heterogeneous catalysis reaction—the dissociation of molecular nitrogen on ruthenium—at density-functional-theory-level accuracy. We find that the vibrational entropy of surface atoms, often neglected in HA for transition metal catalysts, contributes significantly to the reaction barrier. The minimum free energy path for dissociation reveals an “on-top” adsorbed molecular state prior to the transition state. While a previously reported flat-lying molecular metastable state can be identified in the potential energy surface, it is absent in the FES at relevant reaction temperatures. These findings demonstrate the importance of identifying critical points self-consistently on the FES for reactions that involve considerable entropic effects.
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
Funders: U.S. Department of Energy, University of Chicago, Villum Fonden, National Energy Research Scientific Computing Center, Midwest Integrated Center for Computational Materials, Office of Science, Stanford Research Computing Center, Stanford University, Basic Energy Sciences, Advanced Scientific Computing Research, Chemical Sciences, Geosciences, and Biosciences Division, Argonne National Laboratory
Awards: G102163711, G1055550527, G107546177, G1092430646, G1104356622, G122673668, G1286236842, G1286251239, G1313983767, G1414775356, G1445641779, G1505794299, G1664757510, G1677143136, G1816284028, G2037563265, G2101401529, G2142261174, COMPUTATIONAL SCIENCE GRADUATE FELLOWSHIP PROGRAM, G222768804, G2287611682, G262962599, G2638771445, G2887060908, G30714459, G3075337988, G3083819904, G340565428, G365575497, G419881185, G426311843, G42916606, G4501827968, A GRADUATE FELLOWSHIP PROGRAM IN COMPUTATIONAL SCIENCE AND RELATED SCIENCES, G498139845, G5076365615, G5106849484, G5296923526, G5387317844, G5505709228, G5648016710, G5849692847, G5910536577, G650090426, G665223801, G6848031779, G6918803902, G7368700025, G7583137537, G791945238, G805243471, G8098238451, G8429432080, COMPUTATIONAL SCIENCE GRADUATE FELLOWSHIP PROGRAM, G969889393, G993149817
OpenAlex ID: W3114027297