Another interesting application of AI/ML is the design and selection of excipient materials, notably with the process of inverse design. Unlike conventional materials discovery, which centres around conceptualizing a material and eventually characterizing it following an arduous synthesis, inverse design focuses on the desired material function/property as the input and the structure as the output . This allows for a closed-loop process whereby the steps to design, produce, and evaluate new materials can be achieved seamlessly . These methods have demonstrated promising capability to accurately predict and synthesize new materials, such as recent work involving polymers engineered based on desired cloud-point temperatures . Multi-step ML workflows have also achieved sophisticated materials synthesis for inorganic NPs by first exploring a predefined sparse parameter space and then optimizing to extend the parameter space and identify target matches . Mekki-Berrada et al. employed a high-throughput microfluidic platform to generate silver NPs, which tested the results of the ML algorithms and facilitated screening of the initial sparse dataset in addition to more refined screening of the extended dataset . Generally, high-throughput screening approaches can expand the breadth of data that may be modeled through ML methods . The integration of AI into these frameworks makes the process faster, less resource-intensive, and overall more efficient by relying on data-driven learning.