(709f) Materials Informatics for Structure-Property Relationships (MISPR) for Liquid Solutions

In this talk, I will present a general purpose, community supported, open-source computational framework coined MISPR (Materials Informatics for Structure–Property Relationships) for guiding and accelerating materials discovery, optimization, and deployment of complex multicomponent liquid solutions. MISPR can tackle the twin challenges of automating and parallelizing multi-scale molecular simulations and developing machine learning frameworks for advanced scientific applications in chemistry, materials science and engineering. It is high-throughput multi-scale computational infrastructure that seamlessly integrates classical molecular dynamics (MD) simulations with density functional theory (DFT).¹ By enabling high-performance data analytics and coupling between different methods and scales, MISPR addresses critical challenges arising from the needs of automated workflow management and data provenance recording. The major features of MISPR include automated DFT and MD simulations, error handling, derivation of molecular and ensemble properties, and creation of output databases that organize results from individual calculations to enable reproducibility and transparency. I will describe newly developed fully automated DFT and MD workflows implemented in MISPR to compute various electronic properties such as nuclear magnetic resonance chemical shift, binding energy, bond dissociation energy, and redox potential with support for multiple methods such as electron transfer and proton-coupled electron transfer reactions.² The infrastructure also enables the characterization of large-scale ensemble properties by providing MD workflows that calculate a wide range of structural and dynamical properties in liquid solutions and at solid-liquid interfaces. At the backend, the infrastructure interfaces with the Gaussian ³ software which enables electronic structure calculations of chemical systems, and LAMMPS ⁴ open-source code for MD simulations. LAMMPS workflows allow the execution of MD simulations in different ensembles and analysis of the dumped trajectories for various dynamical and structural properties using another open-source codebase developed by our lab called MDPropTools.⁵ MDPropTools is a powerful standalone in-house suite of Python-based post-processing routines, to perform statistical analysis of thermodynamic, structural, and dynamical properties of MD simulations of liquids and solid-liquid interfaces.⁵ Using MISPR and MDPropTools, we published the first publicly available database, ComBat ~2,000 computed QC and MD properties for reported lithium sulfur battery electrolytes composed of solvents spanning 16 different chemical classes.^{6, 7} Another open source database, MICRO consists of ~3000 computationally predicted properties for application in carbon dioxide electroreduction. Integrated machine learning models allow exploration of a larger chemical and parameter space at a significantly higher speed to establish structure-property relationships in multicomponent solutions. MISPR employs the methodologies of materials informatics to facilitate understanding and prediction of phenomenological structure-property relationships, which are crucial to designing novel optimal materials for numerous scientific applications and engineering technologies.

References

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. 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.
3. Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A.; Nakatsuji, H.; Li, X.; Caricato, M.; Marenich, A. V.; Bloino, J.; Janesko, B. G.; Gomperts, R.; Mennucci, B.; Hratchian, H. P.; Ortiz, J. V.; Izmaylov, A. F.; Sonnenberg, J. L.; Williams; Ding, F.; Lipparini, F.; Egidi, F.; Goings, J.; Peng, B.; Petrone, A.; Henderson, T.; Ranasinghe, D.; Zakrzewski, V. G.; Gao, J.; Rega, N.; Zheng, G.; Liang, W.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Throssell, K.; Montgomery Jr., J. A.; Peralta, J. E.; Ogliaro, F.; Bearpark, M. J.; Heyd, J. J.; Brothers, E. N.; Kudin, K. N.; Staroverov, V. N.; Keith, T. A.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, A. P.; Burant, J. C.; Iyengar, S. S.; Tomasi, J.; Cossi, M.; Millam, J. M.; Klene, M.; Adamo, C.; Cammi, R.; Ochterski, J. W.; Martin, R. L.; Morokuma, K.; Farkas, O.; Foresman, J. B.; Fox, D. J. Gaussian 16 Rev. C.01, Wallingford, CT, 2016.
4. Plimpton, S. Fast parallel algorithms for short-range molecular dynamics; Sandia National Labs., Albuquerque, NM (United States): 1993.
5. https://github.com/molmd/mdproptools, 2022.
6. https://github.com/rashatwi/combat.
7. Atwi, R. a. R., Nav Nidhi, Guiding Maps of Solvents for Lithium-Sulfur Batteries via a Computational Data-Driven Approach. . Patterns http://dx.doi.org/10.2139/ssrn.4324048 2023.