Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions
Technological advancements in recent decades have greatly transformed the field of material chemistry. Juxtaposing the accentuating energy demand with the pollution associated, urgent measures are required to ensure energy maximization, while reducing the extended experimental time cycle involved in...
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co. Ltd.
2021-08-01
|
| Series: | Materials Reports: Energy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666935821000847 |
| _version_ | 1828086164497629184 |
|---|---|
| author | Oyawale Adetunji Moses Wei Chen Mukhtar Lawan Adam Zhuo Wang Kaili Liu Junming Shao Zhengsheng Li Wentao Li Chensu Wang Haitao Zhao Cheng Heng Pang Zongyou Yin Xuefeng Yu |
| author_facet | Oyawale Adetunji Moses Wei Chen Mukhtar Lawan Adam Zhuo Wang Kaili Liu Junming Shao Zhengsheng Li Wentao Li Chensu Wang Haitao Zhao Cheng Heng Pang Zongyou Yin Xuefeng Yu |
| author_sort | Oyawale Adetunji Moses |
| collection | DOAJ |
| description | Technological advancements in recent decades have greatly transformed the field of material chemistry. Juxtaposing the accentuating energy demand with the pollution associated, urgent measures are required to ensure energy maximization, while reducing the extended experimental time cycle involved in energy production. In lieu of this, the prominence of catalysts in chemical reactions, particularly energy related reactions cannot be undermined, and thus it is critical to discover and design catalyst, towards the optimization of chemical processes and generation of sustainable energy. Most recently, artificial intelligence (AI) has been incorporated into several fields, particularly in advancing catalytic processes. The integration of intensive data set, machine learning models and robotics, provides a very powerful tool in modifying material synthesis and optimization by generating multifarious dataset amenable with machine learning techniques. The employment of robots automates the process of dataset and machine learning models integration in screening intermetallic surfaces of catalyst, with extreme accuracy and swiftness comparable to a number of human researchers. Although, the utilization of robots in catalyst discovery is still in its infancy, in this review we summarize current sway of artificial intelligence in catalyst discovery, briefly describe the application of databases, machine learning models and robots in this field, with emphasis on the consolidation of these monomeric units into a tripartite flow process. We point out current trends of machine learning and hybrid models of first principle calculations (DFT) for generating dataset, which is integrable into autonomous flow process of catalyst discovery. Also, we discuss catalyst discovery for renewable energy related reactions using this tripartite flow process with predetermined descriptors. |
| first_indexed | 2024-04-11T04:50:30Z |
| format | Article |
| id | doaj.art-e59adb44ddcc412aa8fcf66ca9c144ac |
| institution | Directory Open Access Journal |
| issn | 2666-9358 |
| language | English |
| last_indexed | 2024-04-11T04:50:30Z |
| publishDate | 2021-08-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Materials Reports: Energy |
| spelling | doaj.art-e59adb44ddcc412aa8fcf66ca9c144ac2022-12-27T04:39:07ZengKeAi Communications Co. Ltd.Materials Reports: Energy2666-93582021-08-0113100049Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactionsOyawale Adetunji Moses0Wei Chen1Mukhtar Lawan Adam2Zhuo Wang3Kaili Liu4Junming Shao5Zhengsheng Li6Wentao Li7Chensu Wang8Haitao Zhao9Cheng Heng Pang10Zongyou Yin11Xuefeng Yu12Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, PR ChinaMaterials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, PR ChinaMaterials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, PR China; Physics Department, Bayero University, Kano, 700231, NigeriaMaterials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, PR China; Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China, Ningbo, 315100, PR ChinaMaterials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, PR China; Research School of Chemistry, Australian National University, ACT, 2601, AustraliaMaterials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, PR ChinaMaterials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, PR ChinaMaterials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, PR China; Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China, Ningbo, 315100, PR ChinaMaterials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, PR China; Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China, Ningbo, 315100, PR ChinaMaterials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, PR China; Corresponding author.Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China, Ningbo, 315100, PR China; Corresponding author.Research School of Chemistry, Australian National University, ACT, 2601, Australia; Corresponding author.Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, PR ChinaTechnological advancements in recent decades have greatly transformed the field of material chemistry. Juxtaposing the accentuating energy demand with the pollution associated, urgent measures are required to ensure energy maximization, while reducing the extended experimental time cycle involved in energy production. In lieu of this, the prominence of catalysts in chemical reactions, particularly energy related reactions cannot be undermined, and thus it is critical to discover and design catalyst, towards the optimization of chemical processes and generation of sustainable energy. Most recently, artificial intelligence (AI) has been incorporated into several fields, particularly in advancing catalytic processes. The integration of intensive data set, machine learning models and robotics, provides a very powerful tool in modifying material synthesis and optimization by generating multifarious dataset amenable with machine learning techniques. The employment of robots automates the process of dataset and machine learning models integration in screening intermetallic surfaces of catalyst, with extreme accuracy and swiftness comparable to a number of human researchers. Although, the utilization of robots in catalyst discovery is still in its infancy, in this review we summarize current sway of artificial intelligence in catalyst discovery, briefly describe the application of databases, machine learning models and robots in this field, with emphasis on the consolidation of these monomeric units into a tripartite flow process. We point out current trends of machine learning and hybrid models of first principle calculations (DFT) for generating dataset, which is integrable into autonomous flow process of catalyst discovery. Also, we discuss catalyst discovery for renewable energy related reactions using this tripartite flow process with predetermined descriptors.http://www.sciencedirect.com/science/article/pii/S2666935821000847Material chemistrySustainable energyArtificial intelligenceMachine learning modelsRobotsCatalyst discovery |
| spellingShingle | Oyawale Adetunji Moses Wei Chen Mukhtar Lawan Adam Zhuo Wang Kaili Liu Junming Shao Zhengsheng Li Wentao Li Chensu Wang Haitao Zhao Cheng Heng Pang Zongyou Yin Xuefeng Yu Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions Materials Reports: Energy Material chemistry Sustainable energy Artificial intelligence Machine learning models Robots Catalyst discovery |
| title | Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions |
| title_full | Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions |
| title_fullStr | Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions |
| title_full_unstemmed | Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions |
| title_short | Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions |
| title_sort | integration of data intensive machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy related reactions |
| topic | Material chemistry Sustainable energy Artificial intelligence Machine learning models Robots Catalyst discovery |
| url | http://www.sciencedirect.com/science/article/pii/S2666935821000847 |
| work_keys_str_mv | AT oyawaleadetunjimoses integrationofdataintensivemachinelearningandroboticexperimentalapproachesforaccelerateddiscoveryofcatalystsinrenewableenergyrelatedreactions AT weichen integrationofdataintensivemachinelearningandroboticexperimentalapproachesforaccelerateddiscoveryofcatalystsinrenewableenergyrelatedreactions AT mukhtarlawanadam integrationofdataintensivemachinelearningandroboticexperimentalapproachesforaccelerateddiscoveryofcatalystsinrenewableenergyrelatedreactions AT zhuowang integrationofdataintensivemachinelearningandroboticexperimentalapproachesforaccelerateddiscoveryofcatalystsinrenewableenergyrelatedreactions AT kaililiu integrationofdataintensivemachinelearningandroboticexperimentalapproachesforaccelerateddiscoveryofcatalystsinrenewableenergyrelatedreactions AT junmingshao integrationofdataintensivemachinelearningandroboticexperimentalapproachesforaccelerateddiscoveryofcatalystsinrenewableenergyrelatedreactions AT zhengshengli integrationofdataintensivemachinelearningandroboticexperimentalapproachesforaccelerateddiscoveryofcatalystsinrenewableenergyrelatedreactions AT wentaoli integrationofdataintensivemachinelearningandroboticexperimentalapproachesforaccelerateddiscoveryofcatalystsinrenewableenergyrelatedreactions AT chensuwang integrationofdataintensivemachinelearningandroboticexperimentalapproachesforaccelerateddiscoveryofcatalystsinrenewableenergyrelatedreactions AT haitaozhao integrationofdataintensivemachinelearningandroboticexperimentalapproachesforaccelerateddiscoveryofcatalystsinrenewableenergyrelatedreactions AT chenghengpang integrationofdataintensivemachinelearningandroboticexperimentalapproachesforaccelerateddiscoveryofcatalystsinrenewableenergyrelatedreactions AT zongyouyin integrationofdataintensivemachinelearningandroboticexperimentalapproachesforaccelerateddiscoveryofcatalystsinrenewableenergyrelatedreactions AT xuefengyu integrationofdataintensivemachinelearningandroboticexperimentalapproachesforaccelerateddiscoveryofcatalystsinrenewableenergyrelatedreactions |