Deep learning and computational nanoscience

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This research combines artificial intelligence, machine learning, materials science, and nanotechnology. Work will be carried out in two main areas.******The first line of investigation is focused on using deep neural networks to learn approximate solutions to computationally demanding problems such as highly accurate atomic-scale quantum mechanical simulations. Reducing the computational costs and scaling limitations of such calculations will greatly increase researchers' ability to rapidly search for, design, and evaluate new forms of nanoscale enhanced materials. The proposed method is extremely general and may be adaptable to other forms of numerical simulation such as plasmonics, computational fluid dynamics, and forecasting. This research will develop the method and test it for a wide variety of materials such as water-liquid interfaces, high-pressure solids and liquids, and 2d materials (including nanotubes).******The second line of research will consider whether it is possible to teach an AI agent chemical and physical intuition by way of example. We will use algorithms which have recently been shown to be powerful enough to conquer the game of Go without any human knowledge or intervention. Deep reinforcement Q-learning is an approach to AI which, through a series of simulated experiences, teaches an AI to learn to move within, or exert control over, an environment. We will couple such an agent to physically realistic simulations of chemical processes, giving it control over parameters such as temperature, pressure, and chemical species present. The agent will observe the system the same way that a human does - through a combination of sensors and experiments. If successful, a trained agent will able to develop new processes for producing large-scale amounts of nano-enhanced materials such as nanotubes, quantum dots, or 2d materials. This would be hugely impactful in many fields and would constitute a new approach to chemical and material discovery.