Machine learning and descriptor selection for the computational discovery of metal-organic frameworks

ABSTRACT

Metal-organic frameworks (MOFs), crystalline materials with high internal surface area and pore volume, have demonstrated great potential for many applications. In the past decade, as large number of MOFs have come into existence, there has been an effort to model them using computers. High-throughput screening techniques in tandem with molecular simulations or ab-initio calculations are being used to calculate their properties. However, the number of MOFs that can be hypothetically created are in the millions, and though computer simulations have shown remarkable accuracy, we cannot deploy them for all structures due to their high-computational cost. In this regard, machine learning (ML)-based algorithms have proven to be effective in predicting material properties and reducing the need for expensive calculations. Adopting this methodology can save time and allow researchers to explore materials in unchartered chemical space, thus ushering an era of high-throughput in-silico material design using ML. In this work, we present what is ML, its associated workflow, selecting descriptors, and how it can help build reliable models for discovering MOFs. We present some popular and novel ones. Thereafter, we review some of the recent studies with respect to ML-based implementation for MOF discovery emphasizing descriptors selected and the workflow adopted.

KEYWORDS:

• Metal-organic frameworks
• porous coordination polymers
• molecular simulations
• density-functional theory
• ab-initio calculations
• machine learning
• high-throughput in-silico screening
• computational material design

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.