If Optimizing for general parameters in chemistry is useful, why is it hardly done?

Stefan P. Schmid, Ella Miray Rajaonson, Cher Tian Ser, Mohammad Haddadnia, Shi Xuan Leong, Alan Aspuru-Guzik, Agustinus Kristiadi, Kjell Jorner, Felix Strieth-Kalthoff

Keywords: Bayesian Optimization, Generality, Transferable Optima, Reaction Conditions, Condition Optimization

TL;DR: This work benchmarks the effectiveness of Bayesian optimization in discovering general and transferable optima, at the example of chemical reaction optimization.

Abstract:

General parameters are highly desirable in the natural sciences — e.g., reaction
conditions that enable high yields across a range of related transformations. This
has a significant practical impact since those general parameters can be transfered
to related tasks without the need for laborious and time-intensive re-optimization.
While Bayesian optimization (BO) is widely applied to find optimal parameter
sets for specific tasks, it has remained underused in experiment planning towards
such general optima. In this work, we consider the real-world problem of condi-
tion optimization for chemical reactions to study whether performing generality-
oriented BO can accelerate the identification of general optima, and whether these
optima also translate to unseen examples. This is achieved through a careful for-
mulation of the problem as an optimization over curried functions, as well as
systematic benchmarking of generality-oriented strategies for optimization tasks
on real-world experimental data. Empirically, we find that for generality-oriented
optimization, simple optimization strategies that decouple parameter and task se-
lection perform comparably to more complex ones, and that effective optimization
is merely determined by an effective exploration of both parameter and task space.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Submission Number: 12409