Olympus: a benchmarking framework for noisy optimization and experiment planning
Abstract
Research challenges encountered across science, engineering, and economics can frequently be formulated as optimization tasks. In chemistry and materials science, recent growth in laboratory digitization and automation has sparked interest in optimization-guided autonomous discovery and closed-loop experimentation. Experiment planning strategies based on off-the-shelf optimization algorithms can be employed in fully autonomous research platforms to achieve desired experimentation goals with the minimum number of trials. However, the experiment planning strategy that is most suitable to a scientific discovery task is a priori unknown while rigorous comparisons of different strategies are highly time and resource demanding. As optimization algorithms are typically benchmarked on low-dimensional synthetic functions, it is unclear how their performance would translate to noisy, higher-dimensional experimental tasks encountered in chemistry and materials science. We introduce Olympus, a software package that provides a consistent and easy-to-use framework for benchmarking optimization algorithms against realistic experiments emulated via probabilistic deep-learning models. Olympus includes a collection of experimentally derived benchmark sets from chemistry and materials science and a suite of experiment planning strategies that can be easily accessed via a user-friendly Python interface. Furthermore, Olympus facilitates the integration, testing, and sharing of custom algorithms and user-defined datasets. In brief, Olympus mitigates the barriers associated with benchmarking optimization algorithms on realistic experimental scenarios, promoting data sharing and the creation of a standard framework for evaluating the performance of experiment planning strategies.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 12, 2021
- Source ID
- 10.1088/2632-2153/abedc8
Entities
People
- Alán Aspuru-Guzik
- Elena Liles
- Florian Häse
- Jason E Hein
- Loïc M Roch
- Matteo Aldeghi
- Melodie Christensen
- Riley J. Hickman
Organizations
- Defense Advanced Research Projects Agency
- Natural Sciences and Engineering Research Council
- Office of Naval Research
- Vector Institute