Quantitative Mapping of Molecular Substituents to Macroscopic Properties Enables Predictive Design of Oligoethyleneglycol-Based Lithium Electrolytes

Abstract

<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>

Active learning and neural network potentials accelerate molecular screening of ether-based solvate ionic liquids

“…Virtual screening allows quantification of the ion-ligand binding energy with accurate ab initio simulations by identifying the ligand-ion pose with the lowest energy and comparing with the energies of free ligand and ion. 13 The exhaustive configurational sampling needed to evaluate every ion and ligand pair is computationally demanding, because the ion-ligand potential energy surface (PES) has many local minima. Algorithms such as simulated annealing or seeding gradient-based optimization from different random guesses can perform such global optimization but are typically too costly to be combined with quantum chemical methods.…”