Porous Materials as High Pressure Nano-Reactors: Machine Learning and Multi-Scale Simulation

This project proposes a revolutionary approach to synthesizing materials and chemicals under high-pressure conditions using porous materials to enable reactions using forces found in confined spaces. Traditional high-pressure methods in various industries require expensive multi-stage compressors and are limited by material constraints. This new method leverages the significant high pressures observed within adsorbed fluid or solid films on solid substrates, offering a promising alternative to overcome the limitations of conventional pressure vessels. The significance of this project lies in its potential to transform industrial manufacturing processes. By shifting from traditional, bulk-phase high-pressure generation to localized high-pressure conditions resulting from forces found on the surface of porous materials, the project aims to create more sustainable and economically viable high-pressure environments. This advancement aligns with NSF's mission to promote scientific progress, enhance national health, prosperity, and welfare, and potentially secure national defense. The broader impacts of this project include supporting education in STEM fields. Additionally, the educational component will provide advanced training in multi-scale theoretical and simulation methods, fostering interdisciplinary collaboration and outreach programs aimed at underrepresented groups, including Native American students. The project aims to develop a multiscale simulation framework enhanced by machine learning to simulate high-pressure phenomena in nano-confined phases. By incorporating machine learning, the project intends to refine the accuracy of these simulations and predict various material properties, which will provide insights into the behavior of adsorbed films and nanopores under extreme conditions. This framework seeks to capture the complexity of high-pressure conditions, including the phase structure and electrical properties of materials, thus pushing the boundaries of current simulation capabilities. Additionally, the proposal targets the optimization of organic synthesis reactions under high-pressure conditions to improve yield and rates and guide reaction mechanisms. The project will employ machine learning-driven simulations, focusing on the subtleties introduced by pore width and geometry, to develop a comprehensive understanding of the reactions within these nano-confined spaces. Overall, the integration of advanced simulations and machine learning aims to predict and analyze the behavior of porous materials under extreme conditions, potentially revolutionizing material synthesis and offering a new paradigm in high-pressure manufacturing. 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.