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

Kancharlapalli, Srinivasu; Gopalan, Arun; Haranczyk, Maciej; Snurr, Randall Q.

doi:10.1021/acs.jctc.0c01229

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

Journal Article · Fri Mar 19 00:00:00 EDT 2021 · Journal of Chemical Theory and Computation

DOI:https://doi.org/10.1021/acs.jctc.0c01229· OSTI ID:1807800

^[1]; Gopalan, Arun ^[2]; Haranczyk, Maciej ^[3];

^[2]

Northwestern Univ., Evanston, IL (United States); Bhabha Atomic Research Centre, Mumbai (India)
Northwestern Univ., Evanston, IL (United States)
IMDEA Materials Inst., Madrid (Spain)

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. In conclusion, a strong correlation is observed between the partial atomic charge and the average electronegativity difference between the central atom and its bonded neighbors.

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Research Organization:: Univ. of Minnesota, Minneapolis, MN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division

Grant/Contract Number:: SC0008688; FG02-17ER16362; AC02-05CH11231

OSTI ID:: 1807800

Journal Information:: Journal of Chemical Theory and Computation, Vol. 17, Issue 5; ISSN 1549-9618

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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