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Title: Accelerating Electrolyte Discovery through Materials Informatics and Multiscale Simulations

Accurately predicting the macroscopic properties of complex multicomponent solutions from molecular features remains a grand challenge in engineering and materials science. Experimental trial-and-error and brute-force simulations often fall short due to the vast chemical space and the complexity of functional properties across multiple length and time scales. To address these challenges, we developed MISPR (Materials Informatics for Structure–Property  Relationships)—an open-source, high-throughput, multiscale computational infrastructure MISPR integrates density functional theory (DFT), classical molecular dynamics (CMD), and machine learning (ML) to automate hundreds to thousands of parallelized calculations with minimal manual effort, generating high-fidelity databases of electronic, thermodynamic, structural, and dynamical properties of liquid solutions. In this talk, I will highlight MISPR’s unique capabilities through three applications in electrolyte design for next-generation energy storage: (1) Hybrid DFT–CMD–DFT workflows for predicting stable species in Li-, Na-, and Mg-ion batteries using NMR spectroscopy validation, (2) High-throughput screening for Li–S batteries, leading to the ComBat database (~2000 properties across 16 chemical classes) that guides the design of fluorinated electrolytes with high conductivity, low viscosity, and reduced polysulfide solubility, and (3) Development of machine learning models to predict key physicochemical and transport properties of electrolyte mixtures, enabling rapid screening and optimization of new formulations.  By combining MISPR’s robust computational framework with data-centric analysis, this work aims to accelerate the discovery and optimization of advanced electrolytes and interfaces for next-generation energy technologies.

Prof. Nav Nidhi Rajput is an Assistant Professor of Chemical and Molecular Engineering at Stony Brook University. She received her M.S. and Ph.D. in Chemical Engineering from Louisiana State University, specializing in molecular dynamics of ionic liquids in nanoporous materials for electrochemical applications. As a postdoctoral researcher at Lawrence Berkeley National Laboratory, she helped develop the Electrolyte Genome Project for high-throughput electrolyte simulations. Her research at Stony Brook focuses on materials informatics for understanding and predicting structure–property relationships in liquids and solid/liquid interfaces. She has developed methodologies and software infrastructures for high-throughput computations and data-driven electrolyte discovery, with applications in batteries, supercapacitors, CO₂ electroreduction, and organic electrochemical synthesis.

We offer the seminars to a hybrid audience. 

Please join in-person (see meeting location at the top of this event listing) or via WEBEX