Honegumi让实验科学家零代码用上贝叶斯优化，一键生成可运行脚本。
Honegumi: An Interface for Accelerating the Adoption of Bayesian Optimization in the Experimental Sciences
- 通过动态参数配置界面，自动生成定制化优化脚本。
- 基于Ax平台，支持材料、化学、生物等领域的高效实验设计。
- 配套教程帮助非编程背景研究者快速上手，降低使用门槛。
贝叶斯优化（BO）已成为材料科学、化学和生物学等领域实验设计与决策的重要工具。然而，现有BO库复杂且学习曲线陡峭，使不熟悉机器学习或编程的研究人员望而却步。为此，我们推出Honegumi——一个面向实验科学的友好交互式工具，旨在简化高级贝叶斯优化脚本的创建过程。Honegumi提供动态参数选择网格，用户可配置优化任务的关键参数，系统自动生成可直接使用的、经过单元测试的Python脚本。配套的完整教程涵盖概念与实践指导，弥合理论与应用之间的鸿沟。Honegumi基于Ax平台构建，利用现有先进库的能力，同时重构用户体验，使复杂优化方法对实验研究人员更易获取。通过降低入门门槛并提供教育支持，Honegumi致力于推动贝叶斯优化在多领域的广泛应用。
原文摘要 · Abstract (English)
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.
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