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05-acknowledgements.tex

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