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

Abstract

Molecular details often dictate the macroscopic properties of materials, yet due to their vastly different length scales, relationships between molecular structure and bulk properties can be difficult to predict a priori , 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.e., 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 (e.g., Hammett parameters) may not be available or effective.

Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties

“…A previous study indicates that higher solvation-site connectivity leads to a higher conductivity for PEO-like polymers 27 , whose maximum oxygen percentage is 0.33 for PEO. Our results indicate that an even higher ratio of solvating sites might harm conductivity due to increased glass transition temperature from strong solvating site interactions 45 , 46 . In Fig.…”

How Machine Learning Will Revolutionize Electrochemical Sciences

“…For example, electrolytes with different solvent molecules can be analyzed to map molecular structure to ionic conductivity. 53 Such structure-to-property maps (① in Figure 1 ) reliably compute properties for new structures without having to do explicit physics-based calculations once the map is built. For target property values, these maps can be used in an inverse fashion to identify essential structural attributes for the property targets.…”

“…Atomic- or molecular-scale calculations are performed over a wide range of compounds to map atomic/molecular variations to macroscopically relevant properties. For example, electrolytes with different solvent molecules can be analyzed to map molecular structure to ionic conductivity . Such structure-to-property maps (① in Figure ) reliably compute properties for new structures without having to do explicit physics-based calculations once the map is built.…”

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

“…The numerous datasets generated through high‐throughput calculations and experiments are fed into ML to discover valuable information and hidden correlations, which can be quite challenging in current physical science. Based on high‐quality datasets, the ML models have been shown to have the ability to predict the physicochemical properties of materials (such as ionic conductivity and viscosity [24] ) and assist the design of functional materials (such as drugs, [25, 26] energy materials, [27, 28] porous materials, [29] and small molecules [30] ). Consequently, experimental, theoretical, and data tools have become three indispensable methods in current scientific research and are displaying great potential in battery studies (Figure 1).…”

“…Thes tructure-function relationship of ac omplicated system can, therefore,b ed iscovered and unveiled more efficiently.More importantly,anovel research approach has been established based on ML methods.T his statistically driven design is completely different from conventional theoretical approaches,which mainly involve structure-property calculations or crystal structure prediction. [23] Then umerous datasets generated through high-throughput calculations and experiments are fed into ML to discover valuable information and hidden correlations,w hich can be quite challenging in current physical science.B ased on highquality datasets,the ML models have been shown to have the ability to predict the physicochemical properties of materials (such as ionic conductivity and viscosity [24] )a nd assist the design of functional materials (such as drugs, [25,26] energy materials, [27,28] porous materials, [29] and small molecules [30] ). Consequently,experimental, theoretical, and data tools have become three indispensable methods in current scientific research and are displaying great potential in battery studies (Figure 1).…”