# Transferable and extensible machine learning derived atomic charges for modeling hybrid nanoporous materials

## TL;DR

This paper introduces a machine learning model that accurately predicts atomic charges in nanoporous materials, combining the precision of DDEC with the scalability of Qeq, enabling efficient high-throughput screening.

## Contribution

A novel machine learning approach that accurately predicts atomic charges for nanoporous materials, improving high-throughput screening efficiency and applicability across different material classes.

## Key findings

- Predicted charges have a mean absolute deviation of 0.01e from DDEC charges.
- The model accurately predicts adsorption properties consistent with DDEC-based calculations.
- Effective for both metal-organic frameworks and covalent organic frameworks.

## Abstract

Nanoporous materials have attracted significant interest as an emerging platform for adsorption-related applications. The high-throughput computational screening became a standard technique to access the performance of thousands of candidates, but its accuracy is highly dependent on a partial charge assignment method. In this study, we propose a machine learning model that can reconcile the benefits of two main approaches-the high accuracy of density-derived electrostatic and chemical charge (DDEC) method and the scalability of charge equilibration (Qeq) method. The mean absolute deviation of predicted partial charges from the original DDEC counterparts archive an excellent level of 0.01e. The model, initially designed for metal-organic frameworks, is also capable of assigning charges to another class of nanoporous materials, covalent organic frameworks, with acceptable accuracy. Adsorption properties of carbon dioxide, calculated by means of machine learning derived charges, are consistent with the reference data obtained with DDEC charges.