Advanced computational methods are being actively sought for addressing the challenges associated with discovery and development of new combinatorial material such as formulations. A widely adopted approach involves domain informed high-throughput screening of individual components that can be combined into a formulation. This manages to accelerate the discovery of new compounds for a target application but still leave the process of identifying the right 'formulation' from the shortlisted chemical space largely a laboratory experiment-driven process. We report a deep learning model, Formulation Graph Convolution Network (F-GCN), that can map structure-composition relationship of the individual components to the property of liquid formulation as whole. Multiple GCNs are assembled in parallel that featurize formulation constituents domain-intuitively on the fly. The resulting molecular descriptors are scaled based on respective constituent's molar percentage in the formulation, followed by formalizing into a combined descriptor that represents a complete formulation to an external learning architecture. The use case of proposed formulation learning model is demonstrated for battery electrolytes by training and testing it on two exemplary datasets representing electrolyte formulations vs battery performance -- one dataset is sourced from literature about Li/Cu half-cells, while the other is obtained by lab-experiments related to lithium-iodide full-cell chemistry. The model is shown to predict the performance metrics like Coulombic Efficiency (CE) and specific capacity of new electrolyte formulations with lowest reported errors. The best performing F-GCN model uses molecular descriptors derived from molecular graphs that are informed with HOMO-LUMO and electric moment properties of the molecules using a knowledge transfer technique.

翻译：针对新型组合材料（如配方）的发现与开发挑战，学术界正积极探索先进计算方法。广泛采用的方法是：将领域知识融入对可组合成配方的单个组分进行高通量筛选。这虽能加速目标应用新化合物的发现，但识别短名单化学空间中"正确配方"的过程仍主要依赖实验室实验。我们提出一种深度学习模型——配方图卷积网络（F-GCN），该模型能可将单个组分的结构-成分关系映射为液相配方整体性质。多个GCN并行组装，以领域直觉方式实时提取配方组分特征。所得分子描述符根据各组分在配方中的摩尔百分比进行缩放，进而整合为表征完整配位的组合描述符，并输入外部学习架构。通过两个代表性数据集（分别源于锂/铜半电池文献和碘化锂电池全电池实验数据）对电解液配方与电池性能关系进行训练与测试，展示了所提配方学习模型在电池电解质中的应用。实验表明，该模型能以最低误差预测新电解液配方的库仑效率（CE）和比容量等性能指标。性能最优的F-GCN模型采用基于分子图的分子描述符，并通过知识迁移技术引入HOMO-LUMO能级及分子电矩特性。