Generation of magnetic metal-organic frameworks

TL;DR

This paper develops a machine learning approach using the OMDB to identify highly magnetic metal-organic frameworks, addressing computational challenges in simulating organic materials.

Contribution

It introduces a fine-tuned machine learning model trained on OMDB data to discover novel magnetic MOFs from existing structural prototypes.

Findings

Successfully fine-tuned CHGNet for MOF prediction

Identified new highly magnetic MOFs from structural prototypes

Demonstrated the effectiveness of ML in organic material discovery

Abstract

The potential to utilize metal-organic frameworks as a replacement for rare earth materials as well as in technological applications has prompted increased interested in this material class. The simulation of organic materials, including metal-organic frameworks (MOFs), represents a computational challenge due to an increased average number of atoms in the unit cell. Compounding this challenge, modern materials databases are generally limited to inorganic structures due to their utility in modern technologies such as batteries and integrated circuits. Machine-learning tools appear ideally suited to study these systems. However, organic materials are generally underrepresented in the training sets of foundational models. In this work we leverage the the Organic Materials Database (OMDB) to create a training dataset comprised of more than 15,000 single-point first-principles computations for finetuning machine learned interatomic potentials. Specifically, we fine tune CHGNet and implement a site substitution workflow to identify novel, highly magnetic, MOFs from structural prototypes within the QMOF database.