MDLab: AI frameworks for carbon capture and battery materials
There is a growing urgency to discover better materials that capture CO2 from air and improve battery performance. An important step is to search large databases of materials properties to find examples that resemble known carbon capture agents or electrolytes and then test them for effectiveness. T...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Environmental Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2023.1204690/full |
| _version_ | 1797735968369803264 |
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| author | Bruce Elmegreen Hendrik F. Hamann Benjamin Wunsch Theodore Van Kessel Binquan Luan Tonia Elengikal Mathias Steiner Rodrigo Neumann Barros Ferreira Ricardo Luis Ohta Felipe Lopes Oliveira James L. McDonagh Breanndan O’Conchuir Stamatia Zavitsanou Alexander Harrison Flaviu Cipcigan Geeth de Mel Young-Hye La Vidushi Sharma Dmitry Yu Zubarev |
| author_facet | Bruce Elmegreen Hendrik F. Hamann Benjamin Wunsch Theodore Van Kessel Binquan Luan Tonia Elengikal Mathias Steiner Rodrigo Neumann Barros Ferreira Ricardo Luis Ohta Felipe Lopes Oliveira James L. McDonagh Breanndan O’Conchuir Stamatia Zavitsanou Alexander Harrison Flaviu Cipcigan Geeth de Mel Young-Hye La Vidushi Sharma Dmitry Yu Zubarev |
| author_sort | Bruce Elmegreen |
| collection | DOAJ |
| description | There is a growing urgency to discover better materials that capture CO2 from air and improve battery performance. An important step is to search large databases of materials properties to find examples that resemble known carbon capture agents or electrolytes and then test them for effectiveness. This paper describes novel computational tools for accelerated discovery of solvents, nano-porous materials, and electrolytes. These tools have produced interesting results so far, such as the identification of a relatively isolated location in amine configuration space for the solvents with known carbon capture use, and the demonstration of an end-to-end simulation and process model for carbon capture in MOFs. |
| first_indexed | 2024-03-12T13:06:48Z |
| format | Article |
| id | doaj.art-d549eb903fde4e5e8a3c273393e70b8b |
| institution | Directory Open Access Journal |
| issn | 2296-665X |
| language | English |
| last_indexed | 2024-03-12T13:06:48Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Environmental Science |
| spelling | doaj.art-d549eb903fde4e5e8a3c273393e70b8b2023-08-28T15:04:02ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2023-08-011110.3389/fenvs.2023.12046901204690MDLab: AI frameworks for carbon capture and battery materialsBruce Elmegreen0Hendrik F. Hamann1Benjamin Wunsch2Theodore Van Kessel3Binquan Luan4Tonia Elengikal5Mathias Steiner6Rodrigo Neumann Barros Ferreira7Ricardo Luis Ohta8Felipe Lopes Oliveira9James L. McDonagh10Breanndan O’Conchuir11Stamatia Zavitsanou12Alexander Harrison13Flaviu Cipcigan14Geeth de Mel15Young-Hye La16Vidushi Sharma17Dmitry Yu Zubarev18IBM Research, T. J. Watson Research Center, Yorktown Heights, NY, United StatesIBM Research, T. J. Watson Research Center, Yorktown Heights, NY, United StatesIBM Research, T. J. Watson Research Center, Yorktown Heights, NY, United StatesIBM Research, T. J. Watson Research Center, Yorktown Heights, NY, United StatesIBM Research, T. J. Watson Research Center, Yorktown Heights, NY, United StatesIBM Research, T. J. Watson Research Center, Yorktown Heights, NY, United StatesIBM Research, Rio deo Janeiro, BrazilIBM Research, Rio deo Janeiro, BrazilIBM Research, Rio deo Janeiro, BrazilIBM Research, Rio deo Janeiro, BrazilIBM Research Europe, Hartree Centre, Sci-Tech Daresbury, Warrington, United KingdomIBM Research Europe, Hartree Centre, Sci-Tech Daresbury, Warrington, United KingdomIBM Research Europe, Hartree Centre, Sci-Tech Daresbury, Warrington, United KingdomIBM Research Europe, Hartree Centre, Sci-Tech Daresbury, Warrington, United KingdomIBM Research Europe, Hartree Centre, Sci-Tech Daresbury, Warrington, United KingdomIBM Research Europe, Hartree Centre, Sci-Tech Daresbury, Warrington, United KingdomIBM Research Almaden, San Jose, CA, United StatesIBM Research Almaden, San Jose, CA, United StatesIBM Research Almaden, San Jose, CA, United StatesThere is a growing urgency to discover better materials that capture CO2 from air and improve battery performance. An important step is to search large databases of materials properties to find examples that resemble known carbon capture agents or electrolytes and then test them for effectiveness. This paper describes novel computational tools for accelerated discovery of solvents, nano-porous materials, and electrolytes. These tools have produced interesting results so far, such as the identification of a relatively isolated location in amine configuration space for the solvents with known carbon capture use, and the demonstration of an end-to-end simulation and process model for carbon capture in MOFs.https://www.frontiersin.org/articles/10.3389/fenvs.2023.1204690/fullcarbon capturechemical informaticsaminescomputational chemistrynanoporous materialselectrolytes |
| spellingShingle | Bruce Elmegreen Hendrik F. Hamann Benjamin Wunsch Theodore Van Kessel Binquan Luan Tonia Elengikal Mathias Steiner Rodrigo Neumann Barros Ferreira Ricardo Luis Ohta Felipe Lopes Oliveira James L. McDonagh Breanndan O’Conchuir Stamatia Zavitsanou Alexander Harrison Flaviu Cipcigan Geeth de Mel Young-Hye La Vidushi Sharma Dmitry Yu Zubarev MDLab: AI frameworks for carbon capture and battery materials Frontiers in Environmental Science carbon capture chemical informatics amines computational chemistry nanoporous materials electrolytes |
| title | MDLab: AI frameworks for carbon capture and battery materials |
| title_full | MDLab: AI frameworks for carbon capture and battery materials |
| title_fullStr | MDLab: AI frameworks for carbon capture and battery materials |
| title_full_unstemmed | MDLab: AI frameworks for carbon capture and battery materials |
| title_short | MDLab: AI frameworks for carbon capture and battery materials |
| title_sort | mdlab ai frameworks for carbon capture and battery materials |
| topic | carbon capture chemical informatics amines computational chemistry nanoporous materials electrolytes |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2023.1204690/full |
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