Bayesian Machine Learning
Approach to the Quantification of Uncertainties on
Ab Initio Potential Energy Surfaces
Posted on 2020-06-11 - 20:31
This
work introduces a novel methodology for the quantification
of uncertainties associated with potential energy surfaces (PESs)
computed from first-principles quantum mechanical calculations. The
methodology relies on Bayesian inference and machine learning techniques
to construct a stochastic PES and to express the inadequacies associated
with the ab initio data points and their fit. By combining high fidelity
calculations and reduced-order modeling, the resulting stochastic
surface is efficiently forward propagated via quasi-classical trajectory
and master equation calculations. In this way, the PES contribution
to the uncertainty on predefined quantities of interest (QoIs) is
explicitly determined. This study is done at both microscopic (e.g.,
rovibrational-specific rate coefficients) and macroscopic (e.g., thermal
and chemical relaxation properties) levels. A correlation analysis
is finally applied to identify the PES regions that require further
refinement, based on their effects on the QoI reliability. The methodology
is applied to the study of singlet (11A′) and quintet
(25A′) PESs describing the interaction between O2 molecules and O atoms in their ground electronic state. The
investigation of the singlet surface reveals a negligible uncertainty
on the kinetic properties and relaxation times, which are found to
be in excellent agreement with the ones previously published in the
literature. On the other hand, the methodology demonstrated significant
uncertainty on the quintet surface, due to inaccuracies in the description
of the exchange barrier and the repulsive wall. When forward propagated,
this uncertainty is responsible for the variability of 1 order of
magnitude in the vibrational relaxation time and of factor four in
the exchange reaction rate coefficient, both at 2500 K.
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Venturi, S.; Jaffe, R. L.; Panesi, M. (2020). Bayesian Machine Learning
Approach to the Quantification of Uncertainties on
Ab Initio Potential Energy Surfaces. ACS Publications. Collection. https://doi.org/10.1021/acs.jpca.0c02395