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

TL;DR

Bayesian optimization is a powerful tool for experimental design but remains hard to adopt due to library complexity and steep learning curves. Honegumi provides an interactive selection-grid interface that generates ready-to-run, unit-tested Python BO scripts on the Ax Platform, bridging theory and practice. It couples Jinja-based templating with PyScript in-browser execution and ships a comprehensive educational curriculum with concept and coding tutorials. The approach lowers barriers to entry and accelerates adoption across materials science, chemistry, and biology, with a design that supports future extension to other optimization platforms and domains.

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.

Figures (3)

• Figure 1: Default selection interface available on the Honegumi web page with highlighted tooltip information.
• Figure 2: List of concept tutorials and an example of the formatting.
• Figure 3: Flowchart detailing the honegumi workflow.