Rocketsled: a software library for optimizing high-throughput computational searches

Abstract

A major goal of computation is to optimize an objective for which a forward calculation is possible, but no inverse solution exists. Examples include tuning parameters in a nuclear reactor design, optimizing structures in protein folding, or predicting an optimal materials composition for a functional application. In such instances, directing calculations in an optimal manner is important to obtaining the best possible solution within a fixed computational budget. Here, we introduce Rocketsled, an open-source Python-based software framework to help users optimize arbitrary objective functions. Rocketsled is built upon the existing FireWorks workflow software, which allows its computations to scale to supercomputing centers and for its objective functions to be complex, long-running, and error-prone workflows. Other unique features of Rocketsled include its ability to easily swap out the underlying optimizer, the ability to handle multiple competing objectives, the possibility to inject domain knowledge into the optimizer through feature engineering, incorporation of uncertainty estimates, and its parallelization scheme for running in high-throughput at massive scale. We demonstrate the generality of Rocketsled by applying it to optimize several common test functions (Branin-Hoo, Rosenbrock 2D, and Hartmann 6D). We highlight its potential impact through two example use cases for computational materials science. In a search for photocatalysts for hydrogen production among 18 928 perovskites previously calculated with density functional theory, the untuned Rocketsled Random Forest optimizer explores the search space with approximately 6–28 times fewer calculations than random search. In a search among 7394 materials for superhard candidates, Rocketsled requires approximately 61 times fewer calculations than random search to discover interesting candidates. Thus, Rocketsled provides a practical framework for establishing complex optimization schemes with minimal code infrastructure and enables the efficient exploration of otherwise prohibitively large search spaces.