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arXiv 2602.04435cond-mat.mtrl-scics.LG
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利用增强X射线衍射数据的机器学习驱动的钙钛矿晶体系统预测

Machine Learning-Driven Crystal System Prediction for Perovskites Using Augmented X-ray Diffraction Data

Research and Development Center, Dubai Electricity and Water Authority（迪拜电力和供水局研发中心）

Ansu Mathew, Ahmer A. B. Baloch, Alamin Yakasai, Hemant Mittal, Vivian Alberts, Jayakumar V. Karunamurthy

备注：
37 pages, 7 figures. Author accepted manuscript. Published in Engineering Applications of Artificial Intelligence
发表信息：
Engineering Applications of Artificial Intelligence 164 (2026): 113247
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AI总结：

本研究利用机器学习模型对钙钛矿材料的XRD数据进行晶体系统、点群和空间群预测，通过特征增强技术提升模型性能，实现高准确率的结构表征。

摘要：

从X射线衍射（XRD）光谱预测晶体系统是材料科学中的关键任务，尤其对于钙钛矿材料，因其在光伏、光电子和催化中的广泛应用而著称。在本研究中，我们提出了一种机器学习（ML）驱动的框架，利用包括时间序列森林（TSF）、随机森林（RF）、极端梯度提升（XGBoost）、循环神经网络（RNN）、长短期记忆网络（LSTM）、门控循环单元（GRU）以及简单前馈神经网络（NN）在内的先进模型，对钙钛矿材料的XRD数据进行晶体系统、点群和空间群分类。为解决类别不平衡问题并增强模型鲁棒性，我们整合了特征增强策略，如合成少数类过采样技术（SMOTE）、类别加权、抖动和光谱偏移，以及高效的预处理管道。TSF模型结合SMOTE增强在晶体系统预测中表现出色， Matthews相关系数（MCC）为0.9，F1分数为0.92，准确率为97.76%。对于点群和空间群预测，平衡准确率均超过95%。该模型在对称性不同的类别中表现出色，包括立方晶体系统、点群3m和m-3m以及空间群Pnma和Pnnn。本研究突显了机器学习在基于XRD的结构表征和钙钛矿材料加速发现中的潜力。

英文摘要：

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

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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