Machine-Learning-Enabled Thermochemistry Estimator

Modeling adsorbates on single-crystal metals is critical
in rational
catalyst design and other research that requires detailed thermochemistry.
First-principles simulations via density functional theory (DFT) are
among the prevalent tools to acquire such information about surface
species. While they are highly dependable, DFT calculations often
require intensive computational resources and runtime. These limiting
factors become particularly pronounced when investigating large sets
of complex molecules on heavy noble metals. Consequently, our ability
to explore these species and their corresponding energetics is limited.
In this work, we establish a novel framework that utilizes techniques
including molecular encoding, descriptor synthesis, and machine learning
to overcome the limitation of costly DFT simulations. Simultaneously,
we estimate thermochemical information efficiently at the DFT accuracy
level. More specifically, we translated our training molecules into
text-based identifiers through a simplified molecular-input line-entry
system. Following that, we parametrize our training matrices with
sets of short-range descriptors based on group methods, applying first
the nearest neighbors to account for linear contributions. This is
coupled with the long-range descriptors characterizing second nearest
neighbors to account for nonlinear corrections. Finally, we use linear
regression and machine learning techniques, such as Gaussian process
regressions to regress over the linear and nonlinear matrix systems,
respectively. This is the first work to our knowledge that encompasses
both the first and second nearest neighbors based on the group theory
throughout the featurization, training, and deployment stages. We
trained and validated our models with 459 surface species on Pt(111),
Ru(0001), and Ir(111) surfaces. Results exhibit robust performance
to reproduce the energetics of interest, such as enthalpies, entropies,
and heat capacities, at various temperatures. Notably, the mean absolute
errors can be reduced by 48% during training and 19% during prediction
at a minimum, when compared to the classical group method. Leveraging
the novel framework, our machine-learning-enabled thermochemistry
estimator significantly empowers us to research the thermochemistry
of complex species on metal catalysts.