用机器学习优化小波变换径向分布函数，提升非晶材料结构重建精度。
Enhancing Reconstruction Capability of Wavelet Transform Amorphous Radial Distribution Function via Machine Learning Assisted Parameter Tuning
- 通过可学习参数与约束机制，自动优化小波变换关键参数。
- 在仅25%数据训练下，峰值重建精度超越传统机器学习模型。
- 适合研究非晶锗硒及银-锗硒体系原子结构的科研人员。
理解原子结构至关重要，但非晶材料因无序且非周期性而难解。小波变换径向分布函数（WT-RDF）提供物理驱动框架，能可靠重构二元（Ge₀.₂₅Se₀.₇₅）和三元Agₓ(Ge₀.₂₅Se₀.₇₅)₁₀₀₋ₓ（x = 5, 10, 15, 20, 25）系统的首两阶径向分布函数（RDF）峰形与整体趋势。然而，其幅度精度不足，影响配位数等定量分析，根源在于参数（a, b, Kf, C, Λ）选取不当，这些参数反映非晶中原子相互作用。本研究采用机器学习方法，通过可学习参数优化、参数边界约束与选择性损失函数，构建增强版WT-RDF+框架。该方法显著提升峰形重建精度，且在仅使用25%二元数据集训练时，性能优于基准机器学习模型（如径向基函数RBF和长短期记忆LSTM），后者以从第一性原理分子动力学（AIMD）模拟获得的G(r)为输出，而非通过缩减结构因子SR(q)到G(r)的反演。结果表明，WT-RDF+是锗硒及银-锗硒体系中可靠的径向分布函数重建模型。
原文摘要 · Abstract (English)
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 reconstructing the first and second Radial Distribution Function (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. The shortcoming arises from improper parameter (a, b, Kf, C, and Λ)) selection, as the parameters intrinsically represent atomic interactions within amorphous materials. This study addresses the issue by optimizing WT-RDF parameters using a machine learning approach via learnable parameter optimization, parameter bounding, and selective loss, producing the enhanced WT-RDF+ framework. WT-RDF+ improves the precision of peak reconstructions and outperforms benchmark Machine Learning (ML) models, including Radial Basis Function (RBF) and Long Short-term Memory (LSTM), when trained on only 25% of the binary dataset. Specifically, the machine learning benchmarks are defined as regressors with radial distance r input and G(r) output taken from Ab Initio Molecular Dynamics (AIMD) RDF simulation, not the reduced structure factor SR(q) to G(r) inversions. These results demonstrate that WT-RDF+ is a robust and reliable model for RDF reconstruction of Ge-Se and Ag-Ge-Se family.
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