Combining Deep Learning and Coarse Grained Simulation Methods to Study High-Dimensional NanoBiophysical Systems

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We are in the midst of an artificial intelligence (AI) revolution. Computational advances have culminated in massive strides being taken towards having computers "think". Machine learning (ML) has driven most of these advancements where computational algorithms and methodologies - most notably neural networks - are employed to identify patterns in large data sets and then, based on this training, predict outcomes when given smaller amounts of input data. In the past couple of years ML has fundamentally changed established practices from evaluating medical data to fraud detection to financial prediction. Its impact on scientific research is equally astounding. This grant application focuses on the use of ML to solve mathematical equations. While we understand that physical things and processes can be modelled with mathematical equations, finding the equations is often less challenging than actually solving them. ML provides a highly effective and powerful way to solve equations and produce meaningful answers. We intend to advance ML techniques to solve partial differential equations (PDEs). These are a class of equations that can describe complex processes and environments. PDEs can describe the flow of molecules within nanofluidic devices which have dimensions on the order of nanometers (a nm is around ten thousand times smaller than the width of human hair). They are able to isolate, characterize, and modify single biological molecules such as DNA and proteins. A primary use of these devices is in advanced medical practices such as personalized medicine where risk, diagnosis, and therapeutics are informed by one's genetic makeup. Our goal is to develop machine learning methods and techniques to significantly enhance nanofluidic device research and design. ML methods can solve systems that depend on a large number of factors (i.e., high dimensional), allowing the study of complex systems across all possible situations; this is difficult to do with other approaches. This enables us to efficiently refine current and design new devices for tasks such as identifying unique DNA strands that indicate a particular disease. While our focus is on developing these techniques for nanofluidic devices, this knowledge will translate to a vast array of other scenarios from nuclear reactor design to understanding how cream mixes with coffee. By developing ML as a powerful tool for solving PDEs, our research can also benefit computation-based research in other academic labs as well as industrial R&D. This work will help position Canadian academic research at the forefront of the ML revolution. It also will equip Canadian businesses with a powerful tool for rapid and cost efficient development of their technologies. Through training undergraduate and graduate students, this research program will produce the highly qualified personnel needed to ensure Canada remains at the leading edge of a rapidly evolving and increasingly technological global economy.