Designing functional molecules and advanced materials requires complex design choices: tuning continuous process parameters such as temperatures or flow rates, while simultaneously selecting catalysts or solvents. To date, the development of data-driven experiment planning strategies for autonomous experimentation has largely focused on continuous process parameters, despite the urge to devise efficient strategies for the selection of categorical variables. Here, we introduce Gryffin, a general-purpose optimization framework for the autonomous selection of categorical variables driven by expert knowledge. Gryffin augments Bayesian optimization based on kernel density estimation with smooth approximations to categorical distributions. Leveraging domain knowledge in the form of physicochemical descriptors, Gryffin can significantly accelerate the search for promising molecules and materials. Gryffin can further highlight relevant correlations between the provided descriptors to inspire physical insights and foster scientific intuition. In addition to comprehensive benchmarks, we demonstrate the capabilities and performance of Gryffin on three examples in materials science and chemistry: (i) the discovery of non-fullerene acceptors for organic solar cells, (ii) the design of hybrid organic–inorganic perovskites for light-harvesting, and (iii) the identification of ligands and process parameters for Suzuki–Miyaura reactions. Our results suggest that Gryffin, in its simplest form, is competitive with state-of-the-art categorical optimization algorithms. However, when leveraging domain knowledge provided via descriptors, Gryffin outperforms other approaches while simultaneously refining this domain knowledge to promote scientific understanding.

中文翻译：

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Gryffin：一种基于专家知识的分类变量贝叶斯优化算法

设计功能分子和先进材料需要复杂的设计选择：调整连续工艺参数，如温度或流速，同时选择催化剂或溶剂。迄今为止，尽管迫切需要设计用于选择分类变量的有效策略，但用于自主实验的数据驱动实验规划策略的开发主要集中在连续过程参数上。在这里，我们介绍了Gryffin，这是一种通用优化框架，用于由专家知识驱动的分类变量的自主选择。摹ryffin增强了基于核密度估计的贝叶斯优化与分类分布的平滑近似。利用物理化学描述符形式的领域知识，G ryffin可以显着加速寻找有前途的分子和材料。G ryffin可以进一步突出提供的描述符之间的相关相关性，以激发物理洞察力并培养科学直觉。除了全面的基准测试，我们还展示了 G ryffin的能力和性能关于材料科学和化学中的三个例子：(i) 有机太阳能电池非富勒烯受体的发现，(ii) 用于光收集的混合有机-无机钙钛矿的设计，以及 (iii) 配体和过程的鉴定铃木-宫浦反应的参数。我们的结果表明 G ryffin以其最简单的形式与最先进的分类优化算法竞争。然而，当利用通过描述符提供的领域知识时，Gryffin优于其他方法，同时精炼该领域知识以促进科学理解。