Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal–Organic Frameworks

Computational high-throughput screening using molecular simulations is a powerful tool for identifying top-performing metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges are often required to model the electrostatic interactions between the MO...

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Bibliographic Details
Published in:Journal of chemical theory and computation 2021-05, Vol.17 (5), p.3052-3064
Main Authors: Kancharlapalli, Srinivasu, Gopalan, Arun, Haranczyk, Maciej, Snurr, Randall Q
Format: Article
Language:English
Subjects:
Adsorbates
Chemical bonding
Chemical calculations
Computational efficiency
Computing costs
Condensed Matter, Interfaces, and Materials
Density functional theory
Dipole moments
Drug discovery
Electronegativity
Electronic properties
Forest management
INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Machine learning
Metal organic frameworks
Quadrupoles
Screening
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Summary:Computational high-throughput screening using molecular simulations is a powerful tool for identifying top-performing metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges are often required to model the electrostatic interactions between the MOF and the adsorbate, especially when the adsorption involves molecules with dipole or quadrupole moments such as water and CO2. Although ab initio methods can be used to calculate accurate partial atomic charges, these methods are impractical for screening large material databases because of the high computational cost. We developed a random forest machine learning model to predict the partial atomic charges in MOFs using a small yet meaningful set of features that represent both the elemental properties and the local environment of each atom. The model was trained and tested on a collection of about 320 000 density-derived electrostatic and chemical (DDEC) atomic charges calculated on a subset of the Computation-Ready Experimental Metal–Organic Framework (CoRE MOF-2019) database and separately on charge model 5 (CM5) charges. The model predicts accurate atomic charges for MOFs at a fraction of the computational cost of periodic density functional theory (DFT) and is found to be transferable to other porous molecular crystals and zeolites. A strong correlation is observed between the partial atomic charge and the average electronegativity difference between the central atom and its bonded neighbors.
ISSN:1549-9618
1549-9626
DOI:10.1021/acs.jctc.0c01229