Chemical reaction optimization (RO) is an iterative process that results in large, high-dimensional datasets. Current tools allow for only limited analysis and understanding of parameter spaces, making it hard for scientists to review or follow changes throughout the process. With the recent emergence of using artificial intelligence (AI) models to aid RO, another level of complexity has been added. Helping to assess the quality of a model’s prediction and understand its decision is critical to supporting human-AI collaboration and trust calibration. To address this, we propose CIME4R—an open-source interactive web application for analyzing RO data and AI predictions. CIME4R supports users in (i) comprehending a reaction parameter space, (ii) investigating how an RO process developed over iterations, (iii) identifying critical factors of a reaction, and (iv) understanding model predictions. This facilitates making informed decisions during the RO process and helps users to review a completed RO process, especially in AI-guided RO. CIME4R aids decision-making through the interaction between humans and AI by combining the strengths of expert experience and high computational precision. We developed and tested CIME4R with domain experts and verified its usefulness in three case studies. Using CIME4R the experts were able to produce valuable insights from past RO campaigns and to make informed decisions on which experiments to perform next. We believe that CIME4R is the beginning of an open-source community project with the potential to improve the workflow of scientists working in the reaction optimization domain. To the best of our knowledge, CIME4R is the first open-source interactive web application tailored to the peculiar analysis requirements of reaction optimization (RO) campaigns. Due to the growing use of AI in RO, we developed CIME4R with a special focus on facilitating human-AI collaboration and understanding of AI models. We developed and evaluated CIME4R in collaboration with domain experts to verify its practical usefulness.

中文翻译：

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CIME4R：在其参数空间中探索迭代式、AI 引导的化学反应优化活动


化学反应优化 （RO） 是一个迭代过程，可生成大型高维数据集。当前的工具只允许对参数空间进行有限的分析和理解，这使得科学家很难在整个过程中查看或跟踪变化。随着最近使用人工智能 （AI） 模型来帮助 RO 的出现，复杂性又增加了一个级别。帮助评估模型预测的质量并了解其决策对于支持人类与 AI 的协作和信任校准至关重要。为了解决这个问题，我们提出了 CIME4R——一个用于分析 RO 数据和 AI 预测的开源交互式 Web 应用程序。CIME4R 支持用户 （i） 理解反应参数空间，（ii） 研究 RO 过程如何在迭代中发展，（iii） 识别反应的关键因素，以及 （iv） 理解模型预测。这有助于在 RO 过程中做出明智的决策，并帮助用户审查完整的 RO 流程，尤其是在 AI 引导的 RO 中。CIME4R 通过结合专家经验和高计算精度的优势，通过人与 AI 之间的交互来辅助决策。我们与领域专家一起开发和测试了 CIME4R，并在三个案例研究中验证了它的实用性。使用 CIME4R，专家们能够从过去的 RO 活动中获得有价值的见解，并就下一步进行哪些实验做出明智的决定。我们相信 CIME4R 是一个开源社区项目的开始，有可能改善在反应优化领域工作的科学家的工作流程。 据我们所知，CIME4R 是第一款针对反应优化 （RO） 活动的特殊分析要求量身定制的开源交互式 Web 应用程序。由于 AI 在 RO 中的使用越来越多，我们开发了 CIME4R，特别注重促进人类与 AI 的协作和对 AI 模型的理解。我们与领域专家合作开发和评估了 CIME4R，以验证其实际用途。