Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance

Advanced computational methods are being actively sought
to address
the challenges associated with the discovery and development of new
combinatorial materials, such as formulations. A widely adopted approach
involves domain-informed high-throughput screening of individual components
that can be combined together to form a formulation. This manages
to accelerate the discovery of new compounds for a target application
but still leaves the process of identifying the right “formulation”
from the shortlisted chemical space largely a laboratory experiment-driven
process. We report a deep learning model, the Formulation Graph Convolution
Network (F-GCN), that can map the structure-composition relationship
of the formulation constituents to the property of liquid formulation
as a whole. Multiple GCNs are assembled in parallel that featurize
formulation constituents domain-intuitively on the fly. The resulting
molecular descriptors are scaled based on the respective constituent’s
molar percentage in the formulation, followed by integration into
a combined formulation descriptor that represents the complete formulation
to an external learning architecture. The use case of the proposed
formulation learning model is demonstrated for battery electrolytes
by training and testing it on two exemplary data sets representing
electrolyte formulations vs battery performance: one data set is sourced
from the 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 performance metrics such as Coulombic
efficiency (CE) and specific capacity of new electrolyte formulations
with the lowest reported errors. The best-performing F-GCN model uses
molecular descriptors derived from molecular graphs (GCNs) that are
informed with HOMO–LUMO and electric moment properties of the
molecules using a knowledge transfer technique.