Perturbation Theory–Machine Learning Study of Zeolite Materials Desilication

Zeolites are important materials for research and industrial applications. Mesopores are often introduced by desilication but other properties are also affected, making its optimization difficult. In this work, we demonstrate that Perturbation Theory and Machine Learning can be combined in a PTML multioutput model describing the effects of desilication. The PTML model achieves a notable accuracy (R2 = 0.98) in the external validation and can be useful for the rational design of novel materials.

沸石（Zeolites）是科研与工业应用领域的重要材料。通常通过脱硅处理可引入介孔，但该过程会同时影响材料的其他性能，使得性能优化难度提升。本研究证实，可将微扰理论（Perturbation Theory）与机器学习相结合，构建用于描述脱硅过程影响的PTML多输出模型。该PTML模型在外部验证中展现出优异的预测精度（决定系数R²=0.98），可用于新型材料的理性设计。