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

ABSTRACT: 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

SUBMITTER: Sharma V 

PROVIDER: S-EPMC10685446 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature