Machine Learning Assisted Parameter Tuning on Wavelet Transform Amorphous Radial Distribution Function
This work addresses the challenge of quantitative structural analysis in amorphous materials, particularly Ge-Se systems, for applications in phase-change thin films for next-generation electronics, though it appears incremental as it enhances an existing physics-based method.
The study tackled the amplitude accuracy limitations of the wavelet-transform radial distribution function (WT-RDF) for analyzing amorphous materials by optimizing its parameters with machine learning, resulting in the WT-RDF+ framework that improves peak prediction precision and outperforms benchmark models like RBF and LSTM, even when trained on only 25% of the binary dataset.
Understanding atomic structures is crucial, yet amorphous materials remain challenging due to their irregular and non-periodic nature. The wavelet-transform radial distribution function (WT-RDF) offers a physics-based framework for analyzing amorphous structures, reliably predicting the first and second RDF peaks and overall curve trends in both binary Ge 0.25 Se 0.75 and ternary Ag x(Ge 0.25 Se 0.75)100-x (x=5,10,15,20,25) systems. Despite these strengths, WT-RDF shows limitations in amplitude accuracy, which affects quantitative analyses such as coordination numbers. This study addresses the issue by optimizing WT-RDF parameters using a machine learning approach, producing the enhanced WT-RDF+ framework. WT-RDF+ improves the precision of peak predictions and outperforms benchmark ML models, including RBF and LSTM, even when trained on only 25 percent of the binary dataset. These results demonstrate that WT-RDF+ is a robust and reliable model for structural characterization of amorphous materials, particularly Ge-Se systems, and support the efficient design and development of phase-change thin films for next-generation electronic devices and components.