  Optimization of chemical systems and processes have been enhanced and enabled
by the guidance of algorithms and analytical approaches. While many methods
will systematically investigate how underlying variables govern a given
outcome, there is often a substantial number of experiments needed to
accurately model these relations. As chemical systems increase in complexity,
inexhaustive processes must propose experiments that efficiently optimize the
underlying objective, while ideally avoiding convergence on unsatisfactory
local minima. We have developed the Paddy software package around the Paddy
Field Algorithm, a biologically inspired evolutionary optimization algorithm
that propagates parameters without direct inference of the underlying objective
function. Benchmarked against the Tree of Parzen Estimator, a Bayesian
algorithm implemented in the Hyperopt software Library, Paddy displays
efficient optimization with lower runtime, and avoidance of early convergence.
Herein we report these findings for the cases of: global optimization of a
two-dimensional bimodal distribution, interpolation of an irregular sinusoidal
function, hyperparameter optimization of an artificial neural network tasked
with classification of solvent for reaction components, and targeted molecule
generation via optimization of input vectors for a decoder network. We
anticipate that the facile nature of Paddy will serve to aid in automated
experimentation, where minimization of investigative trials and or diversity of
suitable solutions is of high priority.
