一种机器学习辅助优化梯度二氧化钛纳米管微图案的方法，涉及梯度TiO₂纳米管微图案制备。1)设定相关实验条件制备TiO₂纳米管微图案并进行表征，获取实验数据；2)所得实验数据预处理及机器学习建模；3)机器学习模型进行预测并推荐优化实验方案；4)实验验证预测结果，并补充数据，迭代步骤1)～4)。能自动化的实现扩充样本数据、自我学习、自动训练出满足预设精度的模型；自动构建预测材料“参数‑结构‑性质”主动学习框架，实现材料智能生成和材料逆向设计。能在较少的实验条件下寻得氟化铵/水/丙三醇体系下利用双极氧化法一步制备得到的最大梯度范围的TiO₂纳米管微图案样品及其实验条件。操作简单方便、操作耗时短。

A method for optimizing gradient titania nanotube micropatterns assisted by machine learning, which relates to the preparation of gradient TiO2 nanotube micropatterns. 1) Setting relevant experimental conditions to prepare TiO₂ nanotube micropatterns and characterize them to obtain experimental data; 2) Preprocessing and machine learning modeling of the experimental data obtained; 3) Using machine learning models to predict and recommend optimized experimental schemes; 4) Verifying the prediction results by experiments and supplement the data, and iterate steps 1) to 4). It can automatically expand sample data, self-learn, and automatically train a model that meets the preset accuracy; automatically build a predictive material "parameter-structure-property" active learning framework to achieve intelligent material generation and material reverse design. Under the ammonium fluoride/water/glycerol system, the TiO₂ nanotube micropattern samples with the largest gradient range was prepared by the bipolar oxidation method through less experiments. The operation is simple, convenient and time-saving.