Rapidly expanding machine learning (ML) and artificial intelligence (AI) algorithms offer tools for Materials Science and Engineering (MSE), unprecedented just a few of years ago. Even the most advanced traditional tools today (microfluidics, advanced modeling, and supercomputers) cannot keep up with the opportunities offered by data intensive ML/AI tools. The main barrier to achieve the goals laid out in the Materials Genome Initiative (MGI) strategic plan is the difficulty to access clean, curated, comprehensive, meaningful, and most of all, standardized data that can be used in predictive design and modeling of engineered systems, especially true in highly complex and dynamic interfaces between biology and materials science. The ability to deploy these powerful algorithms in domain sciences has remained limited due to the sheer number of dimensions of the parameter space and enormous variability in the data. With the goal of overcoming the current barriers, this project aims to develop a modular software framework, dubbed Materials Intelligence (Mat-I) towards accelerating discovery and innovation in MSE. By exploiting Mat-I technology, the scientific community has the high likelihood of accelerating research, and the collaborating industry (Microsoft, Amazon, Google, NVIDIA, Real Networks, Proctor and Gamble, Allen Institute for Artificial Intelligence, and Intel) will have the crucial tools to develop materials and methods with tailored bio-nano interfaces at the critical intersection of biology, solid-state systems, and informatics in designing devices such as bionanosensors for cancer diagnostics, biomolecular fuel cells for energy harvesting, and neuromorphic networks towards brain-like computers. The project will educate the next generation of innovative scientists, undergraduates, PhDs, and post-doctoral researchers, bolstering the traditional competitive edge of the US at the world stage.
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.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
