Accessing Numerical Energy Hessians with Graph Neural Network Potentials and Their Application in Heterogeneous Catalysis

• Main Authors: Wander, Brook, Musielewicz, Joseph, Cheula, Raffaele, Kitchin, John R
• Format: Journal Article
• Language: English
• Published: 02-10-2024
• Subjects: Physics - Computational Physics; Physics - Materials Science
• DOI: 10.48550/arxiv.2410.01650

Access to the potential energy Hessian enables determination of the Gibbs
free energy, and certain approaches to transition state search and
optimization. Here, we demonstrate that off-the-shelf pretrained Open Catalyst
Project (OCP) machine learned potentials (MLPs) determine the Hessian with
great success (58 cm$^{-1}$ mean absolute error (MAE)) for intermediates
adsorbed to heterogeneous catalyst surfaces. This enables the use of OCP models
for the aforementioned applications. The top performing model, with a simple
offset correction, gives good estimations of the vibrational entropy
contribution to the Gibbs free energy with an MAE of 0.042 eV at 300 K. The
ability to leverage models to capture the translational entropy was also
explored. It was determined that 94% of randomly sampled systems had a
translational entropy greater than 0.1 eV at 300 K. This underscores the need
to go beyond the harmonic approximation to consider the entropy introduced by
adsorbate translation, which increases with temperature. Lastly, we used MLP
determined Hessian information for transition state search and found we were
able to reduce the number of unconverged systems by 65% to 93% overall
convergence, improving on the baseline established by CatTSunami.