论文标题
锂介导的氨合成的质子供体的闭环设计与可解释的模型和分子机学习
Closed-Loop Design of Proton Donors for Lithium-Mediated Ammonia Synthesis with Interpretable Models and Molecular Machine Learning
论文作者
论文摘要
在这项工作中，我们通过实验确定了几类质子供体在基于四氢呋喃的电解质中锂介导的电化学氮还原的功效，这是一种产生氨的有吸引力的替代方法。然后，我们构建了一个可解释的数据驱动的分类模型，该模型识别溶剂化的kamlet-taft参数对于区分主动质子和非活性质子供体很重要。策划了kamlet-Taft参数的数据集后，我们训练了一个深度学习模型，以预测kamlet-taft参数。分类模型和深度学习模型的组合提供了从给定质子供体到产生氨的能力的预测映射。我们证明，这种分类模型与深度学习的组合优于准确性和实验数据效率的纯机械或数据驱动的方法。
In this work, we experimentally determined the efficacy of several classes of proton donors for lithium-mediated electrochemical nitrogen reduction in a tetrahydrofuran-based electrolyte, an attractive alternative method for producing ammonia. We then built an interpretable data-driven classification model which identified solvatochromic Kamlet-Taft parameters as important for distinguishing between active and inactive proton donors. After curating a dataset for the Kamlet-Taft parameters, we trained a deep learning model to predict the Kamlet-Taft parameters. The combination of classification model and deep learning model provides a predictive mapping from a given proton donor to the ability to produce ammonia. We demonstrate that this combination of classification model with deep learning is superior to a purely mechanistic or data-driven approach in accuracy and experimental data efficiency.
