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The enormous structural and chemical diversity of metal-organic frameworks(MOFs) forces researchers to actively use simulation techniques on an equalfooting with experiments. MOFs are widely known for outstanding adsorptionproperties, so precise description of host-guest interactions is essential forhigh-throughput screening aimed at ranking the most promising candidates.However, highly accurate ab initio calculations cannot be routinely applied tomodel thousands of structures due to the demanding computational costs. On theother side, methods based on force field (FF) parametrization suffer from lowtransferability. To resolve this accuracy-efficiency dilemma, we apply themachine learning (ML) approach. The trained models reproduce atom-in-materialquantities, including partial charges, polarizabilities, dispersioncoefficients, quantum Drude oscillator and electron cloud parameters within theaccuracy of underlying density functional theory method. The aforementioned FFprecursors make it possible to thoroughly describe non-covalent interactionstypical for MOF-adsorbate systems: electrostatic, dispersion, polarization, andshort-range repulsion. The presented approach can also significantly facilitatehybrid atomistic simulations/ML workflows.

Parametrization of Non-Bonded Force Field Terms for Metal-Organic Frameworks Using Machine Learning Approach