Accelerating the prediction of CO2 capture at low partial pressures in metal-organic frameworks using new machine learning descriptors

Ibrahim B. Orhan; Tu C. Le; Ravichandar Babarao; Aaron W. Thornton

doi:10.1038/s42004-023-01009-x

Back

Accelerating the prediction of CO2 capture at low partial pressures in metal-organic frameworks using new machine learning descriptors

Journal article

Open access

Peer reviewed

Accelerating the prediction of CO2 capture at low partial pressures in metal-organic frameworks using new machine learning descriptors

Ibrahim B. Orhan, Tu C. Le, Ravichandar Babarao and Aaron W. Thornton

Communications chemistry, Vol.6, 214

03/10/2023

DOI: https://doi.org/10.1038/s42004-023-01009-x

PMID: 37789142

Appears in Recent Faculty of Science and Engineering Publications

Files and links (2)

pdf

Accelerating the prediction of CO2 capture at low partial pressures in metal-organic frameworks using new machine learning descriptors3.84 MBDownload View

Published (Version of record)CC BY V4.0, Open Access

url

Accelerating the prediction of CO2 capture at low partial pressures in metal-organic frameworks using new machine learning descriptorsView

Published (Version of record)CC BY V4.0, Open

Metrics

1 File views/ downloads

10 Record Views

4 Times Cited - Web of Science

See more details

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

Source: InCites

Abstract

Carbon capture and storage

Computational chemistry

Metal-organic frameworks

Method development

Theoretical and computational chemistry

Artificial intelligence

Metal-Organic frameworks (MOFs) have been considered for various gas storage and separation applications. Theoretically, there are an infinite number of MOFs that can be created; however, a finite amount of resources are available to evaluate each one. Computational methods can be adapted to expedite the process of evaluation. In the context of CO2 capture, this paper investigates the method of screening MOFs using machine learning trained on molecular simulation data. New descriptors are introduced to aid this process. Using all descriptors, it is shown that machine learning can predict the CO2 adsorption, with an R2 of above 0.9. The introduced Effective Point Charge (EPoCh) descriptors, which assign values to frameworks’ partial charges based on the expected CO2 uptake of an equivalent point charge in isolation, are shown to be the second most important group of descriptors, behind the Henry coefficient. Furthermore, the EPoCh descriptors are hundreds of thousands of times faster to obtain compared with the Henry coefficient, and they achieve similar results when identifying top candidates for CO2 capture using pseudo-classification predictions.

Details

Title: Accelerating the prediction of CO2 capture at low partial pressures in metal-organic frameworks using new machine learning descriptors
Creators: Ibrahim B. Orhan - RMIT University
Tu C. Le - RMIT University
Ravichandar Babarao - RMIT University
Aaron W. Thornton - CSIRO Future Industries—Manufacturing Business Unit (Clayton, Victoria)
Publication Details: Communications chemistry, Vol.6, 214
Publisher: Nature Publishing Group UK
Identifiers: 991013150613702368
Academic Unit: Faculty of Science and Engineering; Science
Language: English
Resource Type: Journal article

Accelerating the prediction of CO2 capture at low partial pressures in metal-organic frameworks using new machine learning descriptors

Files and links (2)

Metrics

UN Sustainable Development Goals (SDGs)

Abstract

Details

Southern Cross University Social media