Using Machine Learning to Predict the Adsorption Properties of Thiophene (C₄H₄S)

We present a machine learning (ML) study of the adsorption of thiophene (C₄H₄S) on various single metal and bimetallic (100) transition metal surfaces. We employ the Hierarchically Interacting Particle Neural Network (HIP-NN) to make our predictions. HIP-NN, a deep neural network, uses both local atomic density and pairwise atom information to make predictions. For our training dataset we use a database of over 2400 thiophene adsorption calculations generated with Quantum Espresso using the BEEF-vdW density functional theory (DFT) functional. These configurations have the thiophene molecule adsorbed in a parallel absorption configuration with the molecule centered over a hollow site with the sulfur atom near an atop site. This adsorption site has been shown in previous studies to be the most energetically favorable adsorption site. Overall, we make predictions, using ML, of adsorption energies, adsorption heights, buckling of the surface, and charge transfer from the surface to the S atom to the accuracy of the DFT calculations, and try to correlate these properties, namely charge transfer and adsorption energy, to the hydrodesulfurization rates of the sulfurized version of these surfaces.