Loading…
Accelerated discovery of porous materials for carbon capture by machine learning: A review
In the past decades, greenhouse gases (e.g., anthropogenic CO 2 and CH 4 ) have raised significant concerns due to the foreseeable dire consequences in climate change. Capturing them via adsorption using porous materials has drawn much attention due to their low synthesis and regeneration cost and h...
Saved in:
| Published in: | MRS bulletin 2022-04, Vol.47 (4), p.432-439 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites Items that cite this one |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | cdi_FETCH-LOGICAL-c319t-245e8879e323008578f5ef2d74cd9fb96cac0501a34a20ba43a839b522a3c7d03 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c319t-245e8879e323008578f5ef2d74cd9fb96cac0501a34a20ba43a839b522a3c7d03 |
| container_end_page | 439 |
| container_issue | 4 |
| container_start_page | 432 |
| container_title | MRS bulletin |
| container_volume | 47 |
| creator | Zhang, Chi Xie, Yunchao Xie, Chen Dong, Hongxing Zhang, Long Lin, Jian |
| description | In the past decades, greenhouse gases (e.g., anthropogenic CO
2
and CH
4
) have raised significant concerns due to the foreseeable dire consequences in climate change. Capturing them via adsorption using porous materials has drawn much attention due to their low synthesis and regeneration cost and high capacity. Recently, the flourishing machine learning (ML) has been introduced to various fields of materials science, which also has shown great potential in accelerating the materials discovery for carbon capture. In this article, we first describe the general workflow of applying ML to tackle materials problems. Then we systematically summarize the recent research progress in the application of ML for development of porous carbon and metal–organic frameworks for carbon capture. Finally, we discuss the existing challenges, possible solutions, and research directions. This article will inspire exploration of new frontiers in the carbon capture by development of ML in porous materials research in the future.
Graphical abstract |
| doi_str_mv | 10.1557/s43577-022-00317-2 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2718226170</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2718226170</sourcerecordid><originalsourceid>FETCH-LOGICAL-c319t-245e8879e323008578f5ef2d74cd9fb96cac0501a34a20ba43a839b522a3c7d03</originalsourceid><addsrcrecordid>eNp9kDtPwzAUhS0EEqXwB5gsMRv8iGuHrap4SZVYYGGxHOempGrjcp0U9d9jCBIb0x3O-c6VPkIuBb8WWpubVChtDONSMs6VMEwekYkolWWikPqYTLi1iplZWZySs5TWnAvNjZ6Qt3kIsAH0PdS0blOIe8ADjQ3dRYxDotucYOs3iTYRafBYxS6fXT8g0OqQ8_DedkA34LFru9UtnVOEfQuf5-SkyRxc_N4peb2_e1k8suXzw9NivmRBibJnstBgrSlBScW51cY2GhpZmyLUZVOVs-AD11x4VXjJK18ob1VZaSm9Cqbmakquxt0dxo8BUu_WccAuv3TSCCvlTJjvlhxbAWNKCI3bYbv1eHCCu2-HbnToskP349DJDKkRSrncrQD_pv-hvgBAc3RQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2718226170</pqid></control><display><type>article</type><title>Accelerated discovery of porous materials for carbon capture by machine learning: A review</title><source>Springer Nature:Jisc Collections:Springer Nature Read and Publish 2023-2025: Springer Reading List</source><creator>Zhang, Chi ; Xie, Yunchao ; Xie, Chen ; Dong, Hongxing ; Zhang, Long ; Lin, Jian</creator><creatorcontrib>Zhang, Chi ; Xie, Yunchao ; Xie, Chen ; Dong, Hongxing ; Zhang, Long ; Lin, Jian</creatorcontrib><description>In the past decades, greenhouse gases (e.g., anthropogenic CO
2
and CH
4
) have raised significant concerns due to the foreseeable dire consequences in climate change. Capturing them via adsorption using porous materials has drawn much attention due to their low synthesis and regeneration cost and high capacity. Recently, the flourishing machine learning (ML) has been introduced to various fields of materials science, which also has shown great potential in accelerating the materials discovery for carbon capture. In this article, we first describe the general workflow of applying ML to tackle materials problems. Then we systematically summarize the recent research progress in the application of ML for development of porous carbon and metal–organic frameworks for carbon capture. Finally, we discuss the existing challenges, possible solutions, and research directions. This article will inspire exploration of new frontiers in the carbon capture by development of ML in porous materials research in the future.
Graphical abstract</description><identifier>ISSN: 0883-7694</identifier><identifier>EISSN: 1938-1425</identifier><identifier>DOI: 10.1557/s43577-022-00317-2</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Applied and Technical Physics ; Carbon sequestration ; Characterization and Evaluation of Materials ; Chemistry and Materials Science ; Energy Materials ; Greenhouse gases ; Machine learning ; Materials Engineering ; Materials Science ; Metal-organic frameworks ; Nanotechnology ; Porous materials ; Review Article ; Workflow</subject><ispartof>MRS bulletin, 2022-04, Vol.47 (4), p.432-439</ispartof><rights>The Author(s), under exclusive License to the Materials Research Society 2022</rights><rights>The Author(s), under exclusive License to the Materials Research Society 2022.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-245e8879e323008578f5ef2d74cd9fb96cac0501a34a20ba43a839b522a3c7d03</citedby><cites>FETCH-LOGICAL-c319t-245e8879e323008578f5ef2d74cd9fb96cac0501a34a20ba43a839b522a3c7d03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Zhang, Chi</creatorcontrib><creatorcontrib>Xie, Yunchao</creatorcontrib><creatorcontrib>Xie, Chen</creatorcontrib><creatorcontrib>Dong, Hongxing</creatorcontrib><creatorcontrib>Zhang, Long</creatorcontrib><creatorcontrib>Lin, Jian</creatorcontrib><title>Accelerated discovery of porous materials for carbon capture by machine learning: A review</title><title>MRS bulletin</title><addtitle>MRS Bulletin</addtitle><description>In the past decades, greenhouse gases (e.g., anthropogenic CO
2
and CH
4
) have raised significant concerns due to the foreseeable dire consequences in climate change. Capturing them via adsorption using porous materials has drawn much attention due to their low synthesis and regeneration cost and high capacity. Recently, the flourishing machine learning (ML) has been introduced to various fields of materials science, which also has shown great potential in accelerating the materials discovery for carbon capture. In this article, we first describe the general workflow of applying ML to tackle materials problems. Then we systematically summarize the recent research progress in the application of ML for development of porous carbon and metal–organic frameworks for carbon capture. Finally, we discuss the existing challenges, possible solutions, and research directions. This article will inspire exploration of new frontiers in the carbon capture by development of ML in porous materials research in the future.
Graphical abstract</description><subject>Applied and Technical Physics</subject><subject>Carbon sequestration</subject><subject>Characterization and Evaluation of Materials</subject><subject>Chemistry and Materials Science</subject><subject>Energy Materials</subject><subject>Greenhouse gases</subject><subject>Machine learning</subject><subject>Materials Engineering</subject><subject>Materials Science</subject><subject>Metal-organic frameworks</subject><subject>Nanotechnology</subject><subject>Porous materials</subject><subject>Review Article</subject><subject>Workflow</subject><issn>0883-7694</issn><issn>1938-1425</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kDtPwzAUhS0EEqXwB5gsMRv8iGuHrap4SZVYYGGxHOempGrjcp0U9d9jCBIb0x3O-c6VPkIuBb8WWpubVChtDONSMs6VMEwekYkolWWikPqYTLi1iplZWZySs5TWnAvNjZ6Qt3kIsAH0PdS0blOIe8ADjQ3dRYxDotucYOs3iTYRafBYxS6fXT8g0OqQ8_DedkA34LFru9UtnVOEfQuf5-SkyRxc_N4peb2_e1k8suXzw9NivmRBibJnstBgrSlBScW51cY2GhpZmyLUZVOVs-AD11x4VXjJK18ob1VZaSm9Cqbmakquxt0dxo8BUu_WccAuv3TSCCvlTJjvlhxbAWNKCI3bYbv1eHCCu2-HbnToskP349DJDKkRSrncrQD_pv-hvgBAc3RQ</recordid><startdate>20220401</startdate><enddate>20220401</enddate><creator>Zhang, Chi</creator><creator>Xie, Yunchao</creator><creator>Xie, Chen</creator><creator>Dong, Hongxing</creator><creator>Zhang, Long</creator><creator>Lin, Jian</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>7TA</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope></search><sort><creationdate>20220401</creationdate><title>Accelerated discovery of porous materials for carbon capture by machine learning: A review</title><author>Zhang, Chi ; Xie, Yunchao ; Xie, Chen ; Dong, Hongxing ; Zhang, Long ; Lin, Jian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-245e8879e323008578f5ef2d74cd9fb96cac0501a34a20ba43a839b522a3c7d03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Applied and Technical Physics</topic><topic>Carbon sequestration</topic><topic>Characterization and Evaluation of Materials</topic><topic>Chemistry and Materials Science</topic><topic>Energy Materials</topic><topic>Greenhouse gases</topic><topic>Machine learning</topic><topic>Materials Engineering</topic><topic>Materials Science</topic><topic>Metal-organic frameworks</topic><topic>Nanotechnology</topic><topic>Porous materials</topic><topic>Review Article</topic><topic>Workflow</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Chi</creatorcontrib><creatorcontrib>Xie, Yunchao</creatorcontrib><creatorcontrib>Xie, Chen</creatorcontrib><creatorcontrib>Dong, Hongxing</creatorcontrib><creatorcontrib>Zhang, Long</creatorcontrib><creatorcontrib>Lin, Jian</creatorcontrib><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><jtitle>MRS bulletin</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Chi</au><au>Xie, Yunchao</au><au>Xie, Chen</au><au>Dong, Hongxing</au><au>Zhang, Long</au><au>Lin, Jian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Accelerated discovery of porous materials for carbon capture by machine learning: A review</atitle><jtitle>MRS bulletin</jtitle><stitle>MRS Bulletin</stitle><date>2022-04-01</date><risdate>2022</risdate><volume>47</volume><issue>4</issue><spage>432</spage><epage>439</epage><pages>432-439</pages><issn>0883-7694</issn><eissn>1938-1425</eissn><abstract>In the past decades, greenhouse gases (e.g., anthropogenic CO
2
and CH
4
) have raised significant concerns due to the foreseeable dire consequences in climate change. Capturing them via adsorption using porous materials has drawn much attention due to their low synthesis and regeneration cost and high capacity. Recently, the flourishing machine learning (ML) has been introduced to various fields of materials science, which also has shown great potential in accelerating the materials discovery for carbon capture. In this article, we first describe the general workflow of applying ML to tackle materials problems. Then we systematically summarize the recent research progress in the application of ML for development of porous carbon and metal–organic frameworks for carbon capture. Finally, we discuss the existing challenges, possible solutions, and research directions. This article will inspire exploration of new frontiers in the carbon capture by development of ML in porous materials research in the future.
Graphical abstract</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1557/s43577-022-00317-2</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0883-7694 |
| ispartof | MRS bulletin, 2022-04, Vol.47 (4), p.432-439 |
| issn | 0883-7694 1938-1425 |
| language | eng |
| recordid | cdi_proquest_journals_2718226170 |
| source | Springer Nature:Jisc Collections:Springer Nature Read and Publish 2023-2025: Springer Reading List |
| subjects | Applied and Technical Physics Carbon sequestration Characterization and Evaluation of Materials Chemistry and Materials Science Energy Materials Greenhouse gases Machine learning Materials Engineering Materials Science Metal-organic frameworks Nanotechnology Porous materials Review Article Workflow |
| title | Accelerated discovery of porous materials for carbon capture by machine learning: A review |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-20T16%3A19%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Accelerated%20discovery%20of%20porous%20materials%20for%20carbon%20capture%20by%20machine%20learning:%20A%20review&rft.jtitle=MRS%20bulletin&rft.au=Zhang,%20Chi&rft.date=2022-04-01&rft.volume=47&rft.issue=4&rft.spage=432&rft.epage=439&rft.pages=432-439&rft.issn=0883-7694&rft.eissn=1938-1425&rft_id=info:doi/10.1557/s43577-022-00317-2&rft_dat=%3Cproquest_cross%3E2718226170%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c319t-245e8879e323008578f5ef2d74cd9fb96cac0501a34a20ba43a839b522a3c7d03%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2718226170&rft_id=info:pmid/&rfr_iscdi=true |