Despite the excellent level of maturity of existing computational infrastructure within the scientific community, efforts aimed at integrating hierarchies of paradigms and scales to gain predictive understanding of complex inter- and intra-atomic interactions in electrolyte solutions are still in their infancy. Driven by these needs, we developed a high-throughput computational framework coined MISPR (Materials Informatics for Structure-Property Relationships - https://github.com/molmd/mispr) that seamlessly integrates density functional theory (DFT) calculations with classical molecular dynamics (MD) simulations to robustly predict molecular and ensemble properties in complex multi-component liquid solutions. Functionalities of MISPR include (i) full automation of DFT and MD simulations, (ii) creation of computational databases for establishing structure-property relationships and maintaining data provenance and reproducibility, (iii) automatic error detection and handling, (iv) support for flexible and well-tested DFT workflows for computing properties such as bond dissociation energy, binding energy, and redox potentials, and (v) derivation of ensemble properties such radial distribution functions, ionic conductivity, and residence time. We demonstrate the unique features of MISPR by highlighting on one of its hybrid workflows that predicts stable species in liquid solutions from their nuclear magnetic resonance (NMR) chemical shifts.1 This workflow automatically extracts and categorizes hundreds of thousands of atomic clusters from MD simulations, identifies the most stable species in solution, and calculates their NMR chemical shifts via DFT calculations. Our automated high-throughput framework overcomes various limitations of current NMR computational techniques,2 such as the Edisonian approach to building solvation structures, the difficulty in accounting for representative explicit solvation, and the substantial time required for manual job management.

MISPR: A novel framework for high-throughput multi-scale simulations

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ACS Fall 2022

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