Kinematics Modeling of Peroxy Free Radicals: A Deep Reinforcement
  Learning Approach
Subhadarsi Nayak Hrithwik Shalu Joseph Stember
Tropospheric ozone, known as a concerning air pollutant, has been associated
with health issues including asthma, bronchitis, and impaired lung function.
The rates at which peroxy radicals react with NO play a critical role in the
overall formation and depletion of tropospheric ozone. However, obtaining
comprehensive kinetic data for these reactions remains challenging. Traditional
approaches to determine rate constants are costly and technically intricate.
Fortunately, the emergence of machine learning-based models offers a less
resource and time-intensive alternative for acquiring kinetics information. In
this study, we leveraged deep reinforcement learning to predict ranges of rate
constants (\textit{k}) with exceptional accuracy, achieving a testing set
accuracy of 100%. To analyze reactivity trends based on the molecular structure
of peroxy radicals, we employed 51 global descriptors as input parameters.
These descriptors were derived from optimized minimum energy geometries of
peroxy radicals using the quantum composite G3B3 method. Through the
application of Integrated Gradients (IGs), we gained valuable insights into the
significance of the various descriptors in relation to reaction rates. We
successfully validated and contextualized our findings by conducting
cross-comparisons with established trends in the existing literature. These
results establish a solid foundation for pioneering advancements in chemistry,
where computer analysis serves as an inspirational source driving innovation.