First principles data-driven potentials for prediction of iron carbide clusters
Many have reported the use of quantum chemistry approaches for evaluating the catalytic properties of iron carbide clusters. Unfortunately, structural energy calculations are computationally expensive when using density functional theory. The computational cost is prohibitive for high-throughput sim...
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| Autor*in: |
Enhu Diao [verfasserIn] Yurong He [verfasserIn] Xuhong Liu [verfasserIn] Qiang Tong [verfasserIn] Tao Yang [verfasserIn] Xiaotong Liu [verfasserIn] James P. Lewis [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Frontiers in Quantum Science and Technology ; 2(2023) volume:2 ; year:2023 |
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| Links: |
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| DOI / URN: |
10.3389/frqst.2023.1190522 |
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DOAJ099314703 |
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| 520 | |a Many have reported the use of quantum chemistry approaches for evaluating the catalytic properties of iron carbide clusters. Unfortunately, structural energy calculations are computationally expensive when using density functional theory. The computational cost is prohibitive for high-throughput simulations with large length and time scales. In this paper, we generate data from 177 k clusters and choose state-of-the-art machine learning models within physical chemistry to train the features of this data. The generated potential gives a very high prediction accuracy on the order of the structure stability and achieves better adaptability/tolerance to poor structures of clusters. In addition, we use the machine learning potential to assist in high-throughput data collection and the prediction of hydrogen adsorption sites on cluster surfaces. We achieve more stable adsorption locations of the hydrogen atom more rapidly compared with traditional quantum chemical calculations. | ||
| 650 | 4 | |a iron carbide clusters | |
| 650 | 4 | |a machine learning potentials | |
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10.3389/frqst.2023.1190522 doi (DE-627)DOAJ099314703 (DE-599)DOAJ9663ed7505d44d3fb8d12b7efaa818e8 DE-627 ger DE-627 rakwb eng Enhu Diao verfasserin aut First principles data-driven potentials for prediction of iron carbide clusters 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many have reported the use of quantum chemistry approaches for evaluating the catalytic properties of iron carbide clusters. Unfortunately, structural energy calculations are computationally expensive when using density functional theory. The computational cost is prohibitive for high-throughput simulations with large length and time scales. In this paper, we generate data from 177 k clusters and choose state-of-the-art machine learning models within physical chemistry to train the features of this data. The generated potential gives a very high prediction accuracy on the order of the structure stability and achieves better adaptability/tolerance to poor structures of clusters. In addition, we use the machine learning potential to assist in high-throughput data collection and the prediction of hydrogen adsorption sites on cluster surfaces. We achieve more stable adsorption locations of the hydrogen atom more rapidly compared with traditional quantum chemical calculations. iron carbide clusters machine learning potentials adsorbate prediction cluster optimization high-throughput data analysis Technology T Enhu Diao verfasserin aut Yurong He verfasserin aut Yurong He verfasserin aut Yurong He verfasserin aut Xuhong Liu verfasserin aut Xuhong Liu verfasserin aut Qiang Tong verfasserin aut Qiang Tong verfasserin aut Tao Yang verfasserin aut Xiaotong Liu verfasserin aut James P. Lewis verfasserin aut James P. Lewis verfasserin aut James P. Lewis verfasserin aut In Frontiers in Quantum Science and Technology 2(2023) volume:2 year:2023 https://doi.org/10.3389/frqst.2023.1190522 kostenfrei https://doaj.org/article/9663ed7505d44d3fb8d12b7efaa818e8 kostenfrei https://www.frontiersin.org/articles/10.3389/frqst.2023.1190522/full kostenfrei https://doaj.org/toc/2813-2181 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 2 2023 |
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10.3389/frqst.2023.1190522 doi (DE-627)DOAJ099314703 (DE-599)DOAJ9663ed7505d44d3fb8d12b7efaa818e8 DE-627 ger DE-627 rakwb eng Enhu Diao verfasserin aut First principles data-driven potentials for prediction of iron carbide clusters 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many have reported the use of quantum chemistry approaches for evaluating the catalytic properties of iron carbide clusters. Unfortunately, structural energy calculations are computationally expensive when using density functional theory. The computational cost is prohibitive for high-throughput simulations with large length and time scales. In this paper, we generate data from 177 k clusters and choose state-of-the-art machine learning models within physical chemistry to train the features of this data. The generated potential gives a very high prediction accuracy on the order of the structure stability and achieves better adaptability/tolerance to poor structures of clusters. In addition, we use the machine learning potential to assist in high-throughput data collection and the prediction of hydrogen adsorption sites on cluster surfaces. We achieve more stable adsorption locations of the hydrogen atom more rapidly compared with traditional quantum chemical calculations. iron carbide clusters machine learning potentials adsorbate prediction cluster optimization high-throughput data analysis Technology T Enhu Diao verfasserin aut Yurong He verfasserin aut Yurong He verfasserin aut Yurong He verfasserin aut Xuhong Liu verfasserin aut Xuhong Liu verfasserin aut Qiang Tong verfasserin aut Qiang Tong verfasserin aut Tao Yang verfasserin aut Xiaotong Liu verfasserin aut James P. Lewis verfasserin aut James P. Lewis verfasserin aut James P. Lewis verfasserin aut In Frontiers in Quantum Science and Technology 2(2023) volume:2 year:2023 https://doi.org/10.3389/frqst.2023.1190522 kostenfrei https://doaj.org/article/9663ed7505d44d3fb8d12b7efaa818e8 kostenfrei https://www.frontiersin.org/articles/10.3389/frqst.2023.1190522/full kostenfrei https://doaj.org/toc/2813-2181 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 2 2023 |
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10.3389/frqst.2023.1190522 doi (DE-627)DOAJ099314703 (DE-599)DOAJ9663ed7505d44d3fb8d12b7efaa818e8 DE-627 ger DE-627 rakwb eng Enhu Diao verfasserin aut First principles data-driven potentials for prediction of iron carbide clusters 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many have reported the use of quantum chemistry approaches for evaluating the catalytic properties of iron carbide clusters. Unfortunately, structural energy calculations are computationally expensive when using density functional theory. The computational cost is prohibitive for high-throughput simulations with large length and time scales. In this paper, we generate data from 177 k clusters and choose state-of-the-art machine learning models within physical chemistry to train the features of this data. The generated potential gives a very high prediction accuracy on the order of the structure stability and achieves better adaptability/tolerance to poor structures of clusters. In addition, we use the machine learning potential to assist in high-throughput data collection and the prediction of hydrogen adsorption sites on cluster surfaces. We achieve more stable adsorption locations of the hydrogen atom more rapidly compared with traditional quantum chemical calculations. iron carbide clusters machine learning potentials adsorbate prediction cluster optimization high-throughput data analysis Technology T Enhu Diao verfasserin aut Yurong He verfasserin aut Yurong He verfasserin aut Yurong He verfasserin aut Xuhong Liu verfasserin aut Xuhong Liu verfasserin aut Qiang Tong verfasserin aut Qiang Tong verfasserin aut Tao Yang verfasserin aut Xiaotong Liu verfasserin aut James P. Lewis verfasserin aut James P. Lewis verfasserin aut James P. Lewis verfasserin aut In Frontiers in Quantum Science and Technology 2(2023) volume:2 year:2023 https://doi.org/10.3389/frqst.2023.1190522 kostenfrei https://doaj.org/article/9663ed7505d44d3fb8d12b7efaa818e8 kostenfrei https://www.frontiersin.org/articles/10.3389/frqst.2023.1190522/full kostenfrei https://doaj.org/toc/2813-2181 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 2 2023 |
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10.3389/frqst.2023.1190522 doi (DE-627)DOAJ099314703 (DE-599)DOAJ9663ed7505d44d3fb8d12b7efaa818e8 DE-627 ger DE-627 rakwb eng Enhu Diao verfasserin aut First principles data-driven potentials for prediction of iron carbide clusters 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Many have reported the use of quantum chemistry approaches for evaluating the catalytic properties of iron carbide clusters. Unfortunately, structural energy calculations are computationally expensive when using density functional theory. The computational cost is prohibitive for high-throughput simulations with large length and time scales. In this paper, we generate data from 177 k clusters and choose state-of-the-art machine learning models within physical chemistry to train the features of this data. The generated potential gives a very high prediction accuracy on the order of the structure stability and achieves better adaptability/tolerance to poor structures of clusters. In addition, we use the machine learning potential to assist in high-throughput data collection and the prediction of hydrogen adsorption sites on cluster surfaces. We achieve more stable adsorption locations of the hydrogen atom more rapidly compared with traditional quantum chemical calculations. iron carbide clusters machine learning potentials adsorbate prediction cluster optimization high-throughput data analysis Technology T Enhu Diao verfasserin aut Yurong He verfasserin aut Yurong He verfasserin aut Yurong He verfasserin aut Xuhong Liu verfasserin aut Xuhong Liu verfasserin aut Qiang Tong verfasserin aut Qiang Tong verfasserin aut Tao Yang verfasserin aut Xiaotong Liu verfasserin aut James P. Lewis verfasserin aut James P. Lewis verfasserin aut James P. Lewis verfasserin aut In Frontiers in Quantum Science and Technology 2(2023) volume:2 year:2023 https://doi.org/10.3389/frqst.2023.1190522 kostenfrei https://doaj.org/article/9663ed7505d44d3fb8d12b7efaa818e8 kostenfrei https://www.frontiersin.org/articles/10.3389/frqst.2023.1190522/full kostenfrei https://doaj.org/toc/2813-2181 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 2 2023 |
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First principles data-driven potentials for prediction of iron carbide clusters |
| abstract |
Many have reported the use of quantum chemistry approaches for evaluating the catalytic properties of iron carbide clusters. Unfortunately, structural energy calculations are computationally expensive when using density functional theory. The computational cost is prohibitive for high-throughput simulations with large length and time scales. In this paper, we generate data from 177 k clusters and choose state-of-the-art machine learning models within physical chemistry to train the features of this data. The generated potential gives a very high prediction accuracy on the order of the structure stability and achieves better adaptability/tolerance to poor structures of clusters. In addition, we use the machine learning potential to assist in high-throughput data collection and the prediction of hydrogen adsorption sites on cluster surfaces. We achieve more stable adsorption locations of the hydrogen atom more rapidly compared with traditional quantum chemical calculations. |
| abstractGer |
Many have reported the use of quantum chemistry approaches for evaluating the catalytic properties of iron carbide clusters. Unfortunately, structural energy calculations are computationally expensive when using density functional theory. The computational cost is prohibitive for high-throughput simulations with large length and time scales. In this paper, we generate data from 177 k clusters and choose state-of-the-art machine learning models within physical chemistry to train the features of this data. The generated potential gives a very high prediction accuracy on the order of the structure stability and achieves better adaptability/tolerance to poor structures of clusters. In addition, we use the machine learning potential to assist in high-throughput data collection and the prediction of hydrogen adsorption sites on cluster surfaces. We achieve more stable adsorption locations of the hydrogen atom more rapidly compared with traditional quantum chemical calculations. |
| abstract_unstemmed |
Many have reported the use of quantum chemistry approaches for evaluating the catalytic properties of iron carbide clusters. Unfortunately, structural energy calculations are computationally expensive when using density functional theory. The computational cost is prohibitive for high-throughput simulations with large length and time scales. In this paper, we generate data from 177 k clusters and choose state-of-the-art machine learning models within physical chemistry to train the features of this data. The generated potential gives a very high prediction accuracy on the order of the structure stability and achieves better adaptability/tolerance to poor structures of clusters. In addition, we use the machine learning potential to assist in high-throughput data collection and the prediction of hydrogen adsorption sites on cluster surfaces. We achieve more stable adsorption locations of the hydrogen atom more rapidly compared with traditional quantum chemical calculations. |
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| title_short |
First principles data-driven potentials for prediction of iron carbide clusters |
| url |
https://doi.org/10.3389/frqst.2023.1190522 https://doaj.org/article/9663ed7505d44d3fb8d12b7efaa818e8 https://www.frontiersin.org/articles/10.3389/frqst.2023.1190522/full https://doaj.org/toc/2813-2181 |
| remote_bool |
true |
| author2 |
Enhu Diao Yurong He Xuhong Liu Qiang Tong Tao Yang Xiaotong Liu James P. Lewis |
| author2Str |
Enhu Diao Yurong He Xuhong Liu Qiang Tong Tao Yang Xiaotong Liu James P. Lewis |
| mediatype_str_mv |
c |
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true |
| hochschulschrift_bool |
false |
| doi_str |
10.3389/frqst.2023.1190522 |
| up_date |
2024-07-03T22:10:06.092Z |
| _version_ |
1803597492879622144 |
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7.399913 |