Piezoelectric catalytic process can reduce energy consumption in water treatment processes. However, the design of high-performance piezoelectric materials and the search for operating parameters are still challenging tasks. This study explored a modified machine learning (ML) technology, autoencoded chemical feature interaction machine learning (AutoCFI-ML), by employing the autoencoder and factorization machine for designing piezoelectric materials and optimizing operating parameters to improve the performance of the piezoelectric catalytic process. This method improved the performance of regression-based ML methods through carefully designed chemical features boost. The extreme gradient boosting (XGBoost) model was considered the optimal model with R = 0.88 and RMSE = 1.02. The catalyst composition, initial pH value, catalyst/pollutant dosage ratio, and ultrasound power were identified as relatively important features among the 34 features. When targeting RhB or phenol as the typical pollutant, the errors between the prediction results of the trained AutoCFI-XGBoost model and the experimental results in the reverse experiment were both less than 10%. This work provides novel insights and improvement strategies by ML technique to design piezoelectric catalysts and optimize operating parameters for enhancing the performance of piezoelectric catalytic process, improving the application potential of the piezoelectric catalytic process.

中文翻译：

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自动编码化学特征交互机器学习方法提高压电催化过程的性能

压电催化过程可以降低水处理过程中的能耗。然而，高性能压电材料的设计和工作参数的寻找仍然是具有挑战性的任务。本研究探索了一种改进的机器学习（ML）技术，即自动编码化学特征交互机器学习（AutoCFI-ML），通过采用自动编码器和分解机来设计压电材料并优化操作参数，以提高压电催化过程的性能。该方法通过精心设计的化学特征增强，提高了基于回归的机器学习方法的性能。极限梯度提升 (XGBoost) 模型被认为是最佳模型，R = 0.88，RMSE = 1.02。催化剂组成、初始pH值、催化剂/污染物剂量比和超声功率被认为是34个特征中相对重要的特征。以RhB或苯酚为典型污染物时，训练后的AutoCFI-XGBoost模型的预测结果与逆向实验中的实验结果误差均小于10%。这项工作为利用机器学习技术设计压电催化剂和优化操作参数提供了新颖的见解和改进策略，以提高压电催化过程的性能，提高压电催化过程的应用潜力。