The technical aim of the convergence science team, with expertise in genomics, computer science, physics, and materials science and engineering, is to construct a modular Mat-I software framework towards accelerating discovery and innovation. The research will generate and make accessible comprehensive maps among the input space of structures (peptides and single atomic layer solids, the smallest viable entities in biology and physical sciences, respectively) to the output target space of physical properties under a wide range of experimental conditions. The goal is to learn correlations among the three parameters such that, given the sequence/structure representations and experimental conditions, one can then predict the output physical properties, which may be adapted to complex engineered solutions. The proposed approach will employ, enhance, and develop specific mathematical, statistical, and information approaches for discovery in materials engineering that will combine physical, information, and biosciences. Given a set of measurements, the team will apply ML/AI to make inferences and learn a model of the true underlying process and, using these inferences and quantifications of uncertainty, the team will devise test-beds to maximize the information gained with respect to the model. By collecting data and making correlations in an iterative loop, the pace of discovery will be accelerated in closing the knowledge gaps faster than standard methods. The research will use model selection, robust statistics, and adaptive learning, and prototype validation in both static and dynamic representations of bio-nano interfaces. The project will establish foundational rules of a wide range of key wetware devices for technology and medicine through neural network formation by incorporating biology with solid-state devices of the future, the ultimate goal of the project.