These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
nitrogen_dissociation_ChemArXiv.pdf (2.44 MB)

Neural Network Sampling of the Free Energy Landscape for Nitrogen Dissociation on Ruthenium

revised on 22.12.2020, 16:56 and posted on 23.12.2020, 09:07 by Elizabeth Lee, Thomas Ludwig, Boyuan Yu, Aayush Singh, François Gygi, Jens Kehlet Nørskov, Juan de Pablo

Reaction rates in heterogeneous catalysis are predicted using the free energy profiles of elementary reactions. Conventionally, the energetics are computed from critical points of the potential energy surface, with harmonic free energy corrections. Here we use ab initio molecular dynamics and neural network-assisted enhanced sampling simulations to directly calculate the free energy landscape of a prototypical heterogeneous catalysis reaction, the dissociation of molecular nitrogen on ruthenium. We show that accelerating force- and frequency-based enhanced sampling using neural networks can characterize reactive phenomena at density functional theory-level accuracy. A previously reported molecularly adsorbed metastable state is found in the potential energy surface but is absent in the free energy surface. The potential of mean force for the dissociation reaction shows significant temperature-dependent effects beyond the standard harmonic approximation. We demonstrate that these thermodynamic effects can be important for elementary reactions on transition metal surfaces.


Midwest Integrated Center for Computational Materials (MICCoM) as part of the Computational Materials Sciences Program funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, through Argonne National Laboratory, under Contract No. DE-AC02-06CH11357

Villum Fonden, part of the Villum Center for the Science of Sustainable Fuels and Chemicals (V-SUSTAIN grant 9455)


Advanced Scientific Computing Research

Find out more...

SUNCAT Center for Interface Science and Catalysis as part of the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division, Catalysis Science Program


Email Address of Submitting Author


University of Chicago


United States

ORCID For Submitting Author


Declaration of Conflict of Interest

no conflict of interest