posted on 2024-03-18, 14:37authored byJinwoong Nam, Charanyadevi Ramasamy, Daniel E. Raser, Gustavo L. Barbosa Couto, Lydia Thies, David Hibbitts, Fuat E. Celik
The reliable prediction of properties
for the adsorbates, including
their enthalpy, has been a long-standing challenge as a first key
step in studying surface reactions. It is especially difficult when
large adsorbates are involved as the interactions between the adsorbates
and surface atoms are complex. Here, we developed machine learning
(ML) models for the prediction of the formation enthalpy of various
C2 to C6 hydrocarbon adsorbates on the Pt(111)
surface based on 384 density functional theory calculations. Focusing
on larger and more intricate adsorbates, two-thirds of the total species
were C6 species. Four molecular descriptors that represent
the valency and bonding of individual carbons within the adsorbates
were generated without intensive computation. They were subsequently
used as the features of the ML models with three linear and four nonlinear
algorithms. The models were developed with 30 different samplings
of train/test sets, and their results were statistically analyzed
to ensure the performance of the models. Nonlinear models, especially
kernel ridge regression and extreme gradient boosting, outperformed
linear models with lower absolute errors. The top two accurate models,
based on these algorithms, also displayed remarkable robustness in
predicting various species. Employing ensemble average voting with
these two models, we achieved the lowest mean absolute error of 0.94
kcal/molC. Finally, ML was used to estimate the formation
enthalpy of 3115 hydrocarbon adsorbates on Pt(111), highlighting the
promise of these methods to study more complicated reaction networks.