大窪 貴洋
42 lines
1.5 KiB
TeX
42 lines
1.5 KiB
TeX
%#!latexmk main.tex
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\section*{Acknowledgments}
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This study was supported by JSPS KAKENHI (Grant Numbers 23H04096 and
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24K01597). Part of the computational resources were provided by Fugaku
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at the RIKEN Center for Computational Science (Project IDs: hp250103 and
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hp260055) and by Genkai at the Research Institute for Information
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Technology, Kyushu University.
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% We acknowledge the ChemRxiv preprint server for providing a platform to
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% share the preprint version of this work ({\ChemRxiv}).
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\section*{Supporting Information}
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The Supporting Information is available free of charge.
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\begin{itemize}
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\item Model architecture, descriptor settings, and training
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hyperparameters used to construct the machine-learning potential
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(Table~{\SIdeepmd}).
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\item Reference crystalline structures and the linear regression
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procedure used to calibrate GIPAW-calculated {\nP} chemical shifts
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against experimental values (Fig.~{\SInmrref}).
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\item Radial distribution functions used to determine the cutoff
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distances for defining Zn--O coordination environments and
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coordination numbers (Fig.~{\SIgrZnO}).
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\end{itemize}
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\section*{Data Availability}
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The training data generated using VASP, along with the machine learning
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potential (MLP) constructed for use in MLMD simulations, and glass
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structures obtained by MLMD are available from the Zenodo data
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repository (DOI: xxxxxx/zenodo.xxxxxxxx).
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\section*{Author Information}
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\subsection*{ORCID}
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\noindent Takahiro Ohkubo: 0000-0001-8187-1470
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