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.