用机器学习从XRD图谱预测钙钛矿晶体结构，准确率达97.76%。
Machine Learning-Driven Crystal System Prediction for Perovskites Using Augmented X-ray Diffraction Data
- 融合SMOTE等增强技术，提升不平衡数据下的分类性能。
- 晶体系统预测准确率97.76%，MCC达0.9，点/空间群平衡准确超95%。
- 适合材料发现与自动化结构解析的科研人员使用。
从X射线衍射（XRD）谱图预测晶体结构是材料科学中的关键任务，尤其在钙钛矿材料中具有重要应用价值，广泛用于光伏、光电和催化领域。本文提出一种基于机器学习的框架，采用时间序列森林（TSF）、随机森林（RF）、XGBoost、RNN、LSTM、GRU及简单前馈神经网络（NN），对钙钛矿XRD数据进行晶体系统、点群和空间群分类。为缓解类别不平衡并增强模型鲁棒性，引入了合成少数类过采样技术（SMOTE）、类别加权、抖动和谱图偏移等特征增强策略，并构建高效数据预处理流程。结合SMOTE增强的TSF模型在晶体系统预测中表现优异，达到MCC 0.9、F1分数0.92、准确率97.76%；点群与空间群预测的平衡准确率均超过95%。模型在立方晶系、点群3m与m-3m、空间群Pnma与Pnnn等对称性差异明显的类别上表现尤为突出。本研究展示了机器学习在基于XRD的结构表征与钙钛矿材料加速发现中的潜力。
原文摘要 · Abstract (English)
Prediction of crystal system from X-ray diffraction (XRD) spectra is a critical task in materials science, particularly for perovskite materials which are known for their diverse applications in photovoltaics, optoelectronics, and catalysis. In this study, we present a machine learning (ML)-driven framework that leverages advanced models, including Time Series Forest (TSF), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a simple feedforward neural network (NN), to classify crystal systems, point groups, and space groups from XRD data of perovskite materials. To address class imbalance and enhance model robustness, we integrated feature augmentation strategies such as Synthetic Minority Over-sampling Technique (SMOTE), class weighting, jittering, and spectrum shifting, along with efficient data preprocessing pipelines. The TSF model with SMOTE augmentation achieved strong performance for crystal system prediction, with a Matthews correlation coefficient (MCC) of 0.9, an F1 score of 0.92, and an accuracy of 97.76%. For point and space group prediction, balanced accuracies above 95% were obtained. The model demonstrated high performance for symmetry-distinct classes, including cubic crystal systems, point groups 3m and m-3m, and space groups Pnma and Pnnn. This work highlights the potential of ML for XRD-based structural characterization and accelerated discovery of perovskite materials
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