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Message Passing Neural Networks for Partial Charge Assignment to Metal-Organic Frameworks

preprint
submitted on 13.05.2020 and posted on 14.05.2020 by Ali Raza, Arni Sturluson, Cory Simon, Xiaoli Fern
Virtual screenings can accelerate and reduce the cost of discovering metal-organic frameworks (MOFs) for their applications in gas storage, separation, and sensing. In molecular simulations of gas adsorption/diffusion in MOFs, the adsorbate-MOF electrostatic interaction is typically modeled by placing partial point charges on the atoms of the MOF. For the virtual screening of large libraries of MOFs, it is critical to develop computationally inexpensive methods to assign atomic partial charges to MOFs that accurately reproduce the electrostatic potential in their pores. Herein, we design and train a message passing neural network (MPNN) to predict the atomic partial charges on MOFs under a charge neutral constraint. A set of ca. 2,250 MOFs labeled with high-fidelity partial charges, derived from periodic electronic structure calculations, serves as training examples. In an end-to-end manner, from charge-labeled crystal graphs representing MOFs, our MPNN machine-learns features of the local bonding environments of the atoms and learns to predict partial atomic charges from these features. Our trained MPNN assigns high-fidelity partial point charges to MOFs with orders of magnitude lower computational cost than electronic structure calculations. To enhance the accuracy of virtual screenings of large libraries of MOFs for their adsorption-based applications, we make our trained MPNN model and MPNN-charge-assigned computation-ready, experimental MOF structures publicly available.

Funding

NSF 1920945, NSF 1521687

History

Email Address of Submitting Author

cory.simon@oregonstate.edu

Institution

Oregon State University

Country

USA

ORCID For Submitting Author

0000-0002-8181-9178

Declaration of Conflict of Interest

None

Version Notes

v0: first draft

Exports