Connecting Vibrational Spectroscopy to Atomic Structure via Supervised Manifold Learning: Beyond Peak Analysis,Chemistry of Materials

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Connecting Vibrational Spectroscopy to Atomic Structure via Supervised Manifold Learning: Beyond Peak Analysis
Chemistry of Materials ( IF 7.2 ) Pub Date : 2023-01-24 , DOI: 10.1021/acs.chemmater.2c03207
Daniel Vizoso ₁ , Ghatu Subhash ₂ , Krishna Rajan ₃ , Rémi Dingreville ₁

Affiliation

Vibrational spectroscopy is a nondestructive technique commonly used in chemical and physical analyses to determine atomic structures and associated properties. However, the evaluation and interpretation of spectroscopic profiles based on human-identifiable peaks can be difficult and convoluted. To address this challenge, we present a reliable protocol based on supervised manifold learning techniques meant to connect vibrational spectra to a variety of complex and diverse atomic structure configurations. As an illustration, we examined a large database of virtual vibrational spectroscopy profiles generated from atomistic simulations for silicon structures subjected to different stress, amorphization, and disordering states. We evaluated representative features in those spectra via various linear and nonlinear dimensionality reduction techniques and used the reduced representation of those features with decision trees to correlate them with structural information unavailable through classical human-identifiable peak analysis. We show that our trained model accurately (over 97% accuracy) and robustly (insensitive to noise) disentangles the contribution from the different material states, hence demonstrating a comprehensive decoding of spectroscopic profiles beyond classical (human-identifiable) peak analysis.

中文翻译：

通过监督流形学习将振动光谱学与原子结构联系起来：超越峰值分析

振动光谱学是一种无损技术，常用于化学和物理分析以确定原子结构和相关特性。然而，基于人类可识别峰的光谱图的评估和解释可能很困难且令人费解。为了应对这一挑战，我们提出了一种基于监督流形学习技术的可靠协议，旨在将振动光谱与各种复杂多样的原子结构配置联系起来。作为说明，我们检查了一个大型数据库，该数据库包含虚拟振动光谱分布图，这些分布图是从原子模拟中产生的，用于承受不同应力、非晶化和无序状态的硅结构。我们通过各种线性和非线性降维技术评估了这些光谱中的代表性特征，并使用这些特征的简化表示和决策树将它们与通过经典的人类可识别峰分析无法获得的结构信息相关联。我们表明，我们训练的模型准确（超过 97% 的准确度）和稳健（对噪声不敏感）分离了不同材料状态的贡献，因此展示了超越经典（人类可识别的）峰值分析的光谱轮廓的全面解码。

更新日期：2023-01-24

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