Discovery of Record-Breaking Metal-Organic Frameworks for Methane Storage using Evolutionary Algorithm and Machine Learning

20 May 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

In the past decade, there has been a rise in a number of computational screening works to facilitate finding optimal metal-organic frameworks (MOF) for variety of different applications. Unfortunately, most of these screening works are limited to its initial set of materials and result in brute-force type of a screening approach. In this work, we present a systematic strategy that can find materials with desired property from an extremely diverse and large MOF set of over 100 trillion possible MOFs using machine learning and evolutionary algorithm. It is demonstrated that our algorithm can discover 964 MOFs with methane working capacity over 200 cm3 cm−3 and 96 MOFs with methane working capacity over 208 cm3 cm−3, which is the current world record. We believe that this methodology can facilitate a new type of a screening approach that takes advantage of the modular nature in MOFs, and can readily be extended to other important applications as well.

Keywords

porous material
machine learning
evolutionary algorithm
methane storage

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.