  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.
