Accelerating Non-Empirical Structure Determination of Ziegler–Natta Catalysts with a High-Dimensional Neural Network Potential
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- Hiroki Chikuma
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
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- Gentoku Takasao
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
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- Toru Wada
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
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- Patchanee Chammingkwan
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
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- Jörg Behler
- Lehrstuhl für Theoretische Chemie II, Fakultät für Chemie und Biochemie, Ruhr-Universität Bochum, Bochum D-44780, Germany
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- Toshiaki Taniike
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
書誌事項
- 公開日
- 2023-06-09
- 資源種別
- journal article
- 権利情報
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- https://doi.org/10.15223/policy-029
- https://doi.org/10.15223/policy-037
- https://doi.org/10.15223/policy-045
- DOI
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- 10.1021/acs.jpcc.3c01511
- 公開者
- American Chemical Society (ACS)
この論文をさがす
説明
The determination of catalyst nanostructures with first-principles accuracy using genetic algorithms (GA) is very demanding due to the cubic scaling of the computational cost of density functional theory (DFT) calculations. Here, we demonstrate, for the case of Ziegler–Natta MgCl2/TiCl4 nanoplates, how this structure determination can be accelerated by employing a high-dimensional neural network potential (HDNNP) of essentially DFT accuracy. First, when building HDNNPs for MgCl2/TiCl4 clusters with computationally tractable sizes, we found that the structural diversity in the training set is crucial for obtaining HDNNPs reliably describing the large variety of structures generated by GA. The resulting HDNNPs dramatically accelerated the structure determination while yielding results consistent with DFT. Subsequently, we developed a multistep adaptive procedure to construct a HDNNP for MgCl2/TiCl4 clusters consistent in size and TiCl4 coverage with experiments where prior DFT results were scarcely collected. The structure determination and analyses underline the importance of system size and composition in order to predict some experimentally known facts such as the surface morphology and population of isospecific sites. ; The work of T.T. was supported by JSPS Grants-in-Aid for Scientific Research (grant no. JP 22H01865). The work of H.C. is supported by JST SPRING, (grant no. JPMJSP2102). J.B. is grateful for the financial support by the Japan Advanced Institute of Science and Technology for a visiting professorship.
収録刊行物
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- The Journal of Physical Chemistry C
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The Journal of Physical Chemistry C 127 (24), 11683-11691, 2023-06-09
American Chemical Society (ACS)
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詳細情報 詳細情報について
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- CRID
- 1360021390580253312
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- ISSN
- 19327455
- 19327447
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- HANDLE
- 10754/692510
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- 資料種別
- journal article
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- データソース種別
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- Crossref
- KAKEN
- OpenAIRE
