(588cd) Automating Electrolyte Discovery with Mispr: An Integrated Framework for High-Throughput Simulations and Emerging Data-Driven Tools

The rational design of next-generation electrolytes for energy storage and conversion technologies requires integrated approaches that combine high-throughput simulation, data-driven insights, and experimental feedback. We present MISPR (Materials Informatics for Structure–Property Relationships), an open-source computational infrastructure that automates multiscale modeling workflows to evaluate key electrolyte properties—such as redox stability, solvation structure, viscosity, and ionic conductivity—across vast chemical spaces.¹

MISPR has been used to construct large, curated databases of electrolyte candidates for Li–S batteries.² Automated DFT and MD workflows enable rapid down-selection based on multi-property criteria, accelerating the discovery of promising solvent systems. Notable applications include the design of fluorinated electrolytes with high ionic conductivity and low polysulfide solubility for Li–S batteries, and in silico prediction of solvation structures validated against experimental NMR spectra for multivalent systems.^3-5

Designed to handle complex, multicomponent liquid systems, MISPR is broadly applicable beyond batteries. In the context of CO₂ electroreduction (CO₂RR), we used MISPR to develop a database of ~3000 solvents across 11 chemical classes, combining quantum mechanics and molecular dynamics simulations to identify solvents with high CO₂ solubility and favorable transport properties.

In parallel, we are extending MISPR with machine learning (ML) and natural language processing (NLP) modules to extract insights from the literature and build predictive models from computational data. These emerging tools aim to accelerate materials discovery through intelligent, closed-loop workflows. This talk will highlight MISPR’s current capabilities, its impact on battery electrolyte discovery, and its generalizability to other liquid-phase applications like CO₂RR.

Reference:

1. Atwi, R.; Bliss, M.; Makeev, M.; Rajput, N. N., MISPR: an open-source package for high-throughput multiscale molecular simulations. Scientific Reports 2022, 12 (1), 15760.
2. https://github.com/rashatwi/combat.
3. Atwi, R.; Chen, Y.; Han, K. S.; Mueller, K. T.; Murugesan, V.; Rajput, N. N., An automated framework for high-throughput predictions of NMR chemical shifts within liquid solutions. Nature Computational Science 2022, 2 (2), 112-122.
4. Atwi, R.; Rajput, N. N., Guiding maps of solvents for lithium-sulfur batteries via a computational data-driven approach. Patterns 2023, 4 (9).
5. Rasha Atwi, D. G., Dan Thien Nguyen, Minyung Song, Agrim Babbar, Vijayakumar murugesan, Vilas Pol, Nav Nidhi Rajput, Knowledge-driven design of fluorinated ether electrolytes via a multi-model approach. (under review) 2025.