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

2025-02-04

AI Summary

Existing Bayesian optimization (BO) tools impose high learning barriers for experimental scientists without machine learning expertise, hindering their adoption in domain-specific research. Method: We introduce a low-code BO platform tailored for experimental scientists, built upon Meta Ax. It features a novel visual dynamic grid configuration paradigm enabling interactive parameter specification and automatic generation of executable Python scripts. The platform integrates automated unit test generation and embedded Jupyter-based pedagogical tutorials, establishing a closed-loop workflow spanning theory, configuration, and experimentation. Unlike conventional library wrappers, it fundamentally restructures user workflows. Contribution/Results: Empirical evaluation demonstrates that materials science and biology researchers with zero programming experience can complete their first end-to-end BO task within 30 minutes. The platform has been successfully deployed across multiple real-world experimental domains, enabling rapid, reliable, and accessible optimization for non-expert users.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Learning & Optimization for ROB
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Simplify Bayesian Optimization adoption
• Enhance user-friendly BO scripting
• Bridge theoretical and practical BO implementation

Innovation

• User-friendly BO interface
• Dynamic parameter selection grid
• Comprehensive educational tutorials