Determinants and performance prediction on photocatalytic properties of hydroxyapatite by machine learning

Abstract

Hydroxyapatite (HAP) have been widely used as photocatalysts in environmental remediation. However, the effects of crystallinity, crystal size, and purity of HAP on their photocatalytic performances have not been fully understood yet. In this paper, we synthesized HAP samples with different properties by controlling the calcination conditions and evaluated their photocatalytic performance in the degradation reaction of methyl orange. Combing the catalytic experiments with machine learning technique, we found that the crystallinity of HAP was the dominant parameter that determined the photocatalytic performance. And the developed machine learning method could predict the catalytic results with an error of only 2.2 %. Our work provides a route to predict material properties based its characters by experiment combined with machine learning.