Honegumi: An Interface for Accelerating the Adoption of Bayesian Optimization in the Experimental Sciences

TL;DR

Honegumi is a user-friendly, interactive tool that simplifies creating Bayesian optimization scripts, making advanced methods more accessible to experimental scientists and accelerating their adoption in scientific research.

Contribution

The paper introduces Honegumi, a novel interface that streamlines Bayesian optimization setup and learning, bridging the gap between complex algorithms and experimental researchers.

Findings

Honegumi reduces the complexity of implementing Bayesian optimization.

It provides ready-to-use, tailored Python scripts for researchers.

The tool accelerates adoption of Bayesian optimization in experimental sciences.

Abstract

Bayesian optimization (BO) has emerged as a powerful tool for guiding experimental design and decision-making in various scientific fields, including materials science, chemistry, and biology. However, despite its growing popularity, the complexity of existing BO libraries and the steep learning curve associated with them can deter researchers who are not well-versed in machine learning or programming. To address this barrier, we introduce Honegumi, a user-friendly, interactive tool designed to simplify the process of creating advanced Bayesian optimization scripts. Honegumi offers a dynamic selection grid that allows users to configure key parameters of their optimization tasks, generating ready-to-use, unit-tested Python scripts tailored to their specific needs. Accompanying the interface is a comprehensive suite of tutorials that provide both conceptual and practical guidance, bridging the gap between theoretical understanding and practical implementation. Built on top of the Ax platform, Honegumi leverages the power of existing state-of-the-art libraries while restructuring the user experience to make advanced BO techniques more accessible to experimental researchers. By lowering the barrier to entry and providing educational resources, Honegumi aims to accelerate the adoption of advanced Bayesian optimization methods across various domains.