Antimicrobial Polymer Design by Machine Learning

Polymers are an important class of materials with vast arrays of physical and chemical properties and have been widely used in many applications and industrial products. The rapid growth of resistant microorganisms has caused serious public health issue and poses great pressure on the current healthcare system. In this environment, the necessity of new antibiotic materials is even more prominent. Antimicrobial polymers are a type of polymers that has the ability to eradicate or impede the proliferation of microbes on their surfaces or within their surrounding environment. The mechanism of action of antibacterial polymers makes them a perfect fit for medical devices. Although there have been many successful polymer design studies, the design of new antibacterial polymer with desired antimicrobial properties is still challenging. The pace of material discovery research can be accelerated to meet the high demand for new, functional materials. With the advanced development of artificial intelligence, the use of machine learning has shown great potential in data-driven design and the discovery of polymers up to date. Several polymer datasets have been compiled, allowing robust Machine Learning models to be trained and provide accurate predictions of various polymer properties. Such models are useful for screening promising candidate polymers with high-performing properties prior to lab synthesis. In this project, we focus on the most critical components of polymer design using molecular descriptors and machine learning algorithms. A summary of existing polymer databases and machine learning algorithms is provided, the different categories of polymer descriptors are discussed in detail and multiple past polymer studies cases are analysed. Additionally, we explored ChatGPT, an advanced language model with remarkable capabilities for understanding and responding to text input for polymer design. Furthermore, multiple predictive models were designed and trained based on our database to classify the antimicrobial properties of these polymers.