<p>Molecular details often dictate the macroscopic properties of materials, yet, due to their vastly different length scales, relationships between molecular structure and bulk properties are often difficult to predict <i>a priori</i>, requiring Edisonian optimizations and preventing rational design. Here, we introduce an easy-to-execute strategy based on linear free energy relationships (LFERs) that enables quantitative correlation and prediction of how molecular modifications, <i>i.e.</i>, substituents, impact the ensemble properties of materials. First, we developed substituent parameters based on inexpensive, DFT-computed energetics of elementary pairwise interactions between a given substituent and other constant components of the material. These substituent parameters were then used as inputs to regression analyses of experimentally measured bulk properties, generating a predictive statistical model. We applied this approach to a widely studied class of electrolyte materials: oligo-ethylene glycol (OEG)-LiTFSI mixtures; the resulting model enables elucidation of fundamental physical principles that govern the properties of these electrolytes and also enables prediction of the properties of novel, improved OEG-LiTFSI-based electrolytes. The framework presented here for using context-specific substituent parameters will potentially enhance the throughput of screening new molecular designs for next-generation energy storage devices and other materials-oriented contexts where classical substituent parameters (<i>e.g.</i>, Hammett parameters) may not be available or effective.</p>
Quantitative Mapping of Molecular Substituents to Macroscopic Properties Enables Predictive Design of Oligoethyleneglycol-Based Lithium Electrolytes
