The invention discloses a method for optimizing a gradient
titanium dioxide
nanotube micro-pattern under the assistance of
machine learning, and relates to preparation of the gradient
titanium dioxide
nanotube micro-pattern. The method comprises the following steps: 1) setting related experimental conditions to prepare a TiO2
nanotube micro-pattern and characterize the TiO2 nanotube micro-pattern to obtain experimental data; 2) preprocessing the obtained experimental data and performing
machine learning modeling; 3) performing, by the
machine learning model, prediction and recommending an optimization experiment scheme; and (4) verifying a prediction result through an experiment, supplementing data, and iterating the steps (1)-(4). By means of the technical scheme, sample
data expansion, self-learning and automatic training of a model meeting preset precision can be automatically realized, and then an active learning framework for predicting the parameter
structure property of the material is automatically constructed, and intelligent generation and reverse design of the material are realized. The TiO2 nanotube micro-pattern sample with the maximum gradient range, which is prepared in one step by utilizing a bipolar oxidation method under an
ammonium fluoride / water /
glycerol system, and the experimental conditions of the TiO2 nanotube micro-pattern sample can be found under fewer experimental conditions. Operation is simple and convenient, and
operation time is short.