Predictive Multiscale Modeling of Complex Systems for Sustainability
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Sustainability of chemical process requires more energy-efficient processes, utilization of renewable energy such as solar and wind to drive reactions and separations, better catalysts to improve activity and selectivity and thus to reduce separation cost and energy demand, new technologies that are more efficient, and our ability to tap into underutilized and renewable resources, such as offshore and stranded gas, biogas, and biomass. The distributed nature of many underutilized and renewable resources and the low energy density begs for distributed manufacturing, which can be achieved with modular systems and process intensification, such as plants on wheels. The design of such systems needs much more intimate process integration with high fidelity models. A cross cutting need in all of these systems is the need for better materials, whether catalysts, adsorbents, battery materials, or electrocatalysts, to improve performance, reduce cost, catalyst stability, and robustness. Over the past two decades, multiscale modeling has advanced tremendously, and several algorithms currently exist. Yet, our ability to apply first principles modeling to process design is seriously limited due to multiple challenges. In this talk, we will outline these challenges and introduce computational methods to overcome of them. Specifically, we will discuss how to handle complex reaction networks with first principles accuracy but at a very low computational cost, how to estimate and reduce errors in multiscale models, how to determine the active site of a catalyst, and how to predict novel combinations of active sites to drive activity and selectivity. The concepts of small data, correlations in energies and entropies, correlative uncertainty quantification, machine learning for catalysis, atomistic optimization for improved activity and stability, will be discussed. These concepts will be illustrated with examples focusing on ammonia decomposition chemistry and electrocatalysis.