Machine Learning Accelerated Discovery of Antimicrobial Inorganic Nanomaterials

The growing prevalence of infectious diseases and the increasing threat of bacterial resistance have drawn widespread attention to antimicrobial inorganic nanomaterials. However, the diversity, abundance, and complex mechanisms of these materials present significant challenges in identifying new agents that are both efficient and cost-effective with broad-spectrum activity. In response, this study applied machine learning for the first time to discover antimicrobial inorganic nanomaterials. Information on over 2,000 antimicrobial nanomaterials was extracted from more than 8,000 papers. An unsupervised machine learning analysis was conducted to assess data distribution and explore the relationships between material features and antimicrobial activity in high-dimensional space. A series of machine learning models were trained. Through the evaluation of six performance metrics, five key features were identified from 27 dimensions. To further quantify the structure-activity relationships, a genetic programming-symbolic classification model was employed to generate a precise mathematical formula with a prediction accuracy of 0.83. Using this formula, 43 new antimicrobial inorganic nanomaterials were predicted. Of these, four nanomaterials were synthesized and their antibacterial properties were experimentally validated. This work not only provides a next generation approach for designing antimicrobial inorganic nanomaterials but also opens new avenues for applying machine learning in materials science.