Combining High-Throughput Experiments and Active Learning to Characterize Deep Eutectic Solvents,ACS Sustainable Chemistry & Engineering

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Combining High-Throughput Experiments and Active Learning to Characterize Deep Eutectic Solvents
ACS Sustainable Chemistry & Engineering ( IF 7.1 ) Pub Date : 2024-09-10 , DOI: 10.1021/acssuschemeng.4c04507
Dinis O. Abranches ₁ , William Dean ₂ , Miguel Muñoz ₂ , Wei Wang ₃ , Yangang Liang ₃ , Burcu Gurkan ₂ , Edward J. Maginn ₁ , Yamil J. Colón ₁

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The high tunability of deep eutectic solvents (DESs) stems from the ease of changing their precursors and relative compositions. However, measuring the physicochemical properties across large composition and temperature ranges, necessary to properly design target-specific DESs, is tedious and error-prone and represents a bottleneck in the advancement and scalability of DES-based applications. As such, active learning (AL) methodologies based on Gaussian processes (GPs) were developed in this work to minimize the experimental effort necessary to characterize DESs. Owing to its importance for large-scale applications, the reduction of DES viscosity through the addition of a low-molecular-weight solvent was explored as a case study. A high-throughput experimental screening was initially performed on nine different ternary DESs. Then, GPs were successfully trained to predict DES viscosity from its composition and temperature, showcasing the ability of these stochastic, nonparametric models to accurately describe the physicochemical properties of complex mixtures. Finally, the ability of GPs to provide estimates of their own uncertainty was leveraged through an AL framework to minimize the number of data points necessary to obtain accurate viscosity modes. This led to a significant reduction in data requirements, with many systems requiring only five independent viscosity data points to be properly described.

中文翻译：

结合高通量实验和主动学习来表征低共熔溶剂

低共熔溶剂 (DES) 的高度可调性源于其前体和相关成分易于改变。然而，测量大成分和温度范围内的物理化学性质是正确设计特定目标 DES 所必需的，但这项工作非常繁琐且容易出错，并且是基于 DES 的应用的进步和可扩展性的瓶颈。因此，在这项工作中开发了基于高斯过程 (GP) 的主动学习 (AL) 方法，以最大限度地减少表征 DES 所需的实验工作。由于其对大规模应用的重要性，通过添加低分子量溶剂降低 DES 粘度作为案例研究。最初对九种不同的三元 DES 进行了高通量实验筛选。然后，GP 成功地经过训练，可以根据 DES 的成分和温度来预测 DES 粘度，展示了这些随机、非参数模型准确描述复杂混合物的物理化学性质的能力。最后，通过 AL 框架利用 GP 提供自身不确定性估计的能力，以最大限度地减少获得准确粘度模式所需的数据点数量。这导致数据需求显着减少，许多系统只需要五个独立的粘度数据点即可正确描述。

更新日期：2024-09-10

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