Datasets
Visit this page to access datasets generated in our lab.
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QM Datasets for Machine Learning Potentials:
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Avula et al. J. Phys. Chem. Lett. 2023: Understanding the Anomalous Diffusion of Water in Aqueous Electrolytes Using Machine Learned Potentials.
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DP models and Input files :
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Debendra et al. J. Chem. Phys. 2025: Slowly quenched, high pressure glassy B2O3 at DFT accuracy.
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DEME-TFSI Ionic liquid's refined classical force field:
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Avula et al. J. Chem. Theo. Comput. 2021: Efficient Parametrization of Force Field for the Quantitative Prediction of the Physical Properties of Ionic Liquid Electrolytes.
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Refined force field for Imidazolium based Ionic liquids:
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Mondal et al. J. Phys. Chem. B 2014: Quantitative Prediction of Physical Properties of Imidazolium Based Room Temperature Ionic Liquids through Determination of Condensed Phase Site Charges: A Refined Force Field (Link)
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Mondal et al. J. Chem. Eng. Data 2014: A Molecular Dynamics Study of Collective Transport Properties of Imidazolium-Based Room-Temperature Ionic Liquids (Link)
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Mondal et al. J. Phys. Chem. B 2015: A Refined All-Atom Potential for Imidazolium-Based Room Temperature Ionic Liquids: Acetate, Dicyanamide, and Thiocyanate Anions (Link)
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Link to the force field parameter files (LAMMPS format)
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Refined force field for Liquid Sulfolane:
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​Mukherji et al. ACS Omega 2020: Refined Force Field for Liquid Sulfolane with Particular Emphasis to Its Transport Characteristics (Link)
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Link to the force field parameter files (GROMACS format)
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Refined force field for Liquid Sulfolane:
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​Gaur et al. Phys. Chem. Chem. Phys. 2022: Liquid ethylene glycol: prediction of physical properties, conformer population and interfacial enrichment with a refined non-polarizable force field (Link)
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Link to the force field parameter files (GROMACS format)
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