CAREER: Computational design of sustainable hydrogenation systems via a novel combination of data science, optimization, and ab initio methods

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Sustainable, safe, and process-intensified hydrogenation technologies are essential for distributed, small-scale, and on-demand manufacturing of chemicals and fuels from shale gas and biomass, upgrading carbon dioxide to useful organic chemicals, and upcycling plastic waste. New technological developments in this area would contribute to increasing international competitiveness of the U.S. chemical manufacturing industries and meeting relevant U.N. goals on sustainable development. A promising chemistry to this end is catalytic transfer hydrogenation (CTH), a process that is carried out using hydrogen donors instead of pure molecular H2, thereby offering a safe, H2- and potentially CO2-free hydrogenation technology. A critical step towards deploying CTH is to optimally design the underlying process, a challenging task because atomic-scale information such as reaction thermodynamics, pathways, and rates have implications at the microscopic (e.g., product yield) and macroscopic levels (e.g., process economics). The research vision of this project is to develop and apply novel computational tools, in synergy with experiments, to design CTH processes by integrating information and decisions across the different size scales. In parallel with this research, the educational vision of this project is to promote computational thinking and programming literacy at various levels of STEM education. These two skills are well-recognized as being essential for the next generation of science and engineering innovators to tackle emerging grand challenges in the energy, health, and environmental spheres.This CAREER proposal specifically aims to computationally design a vapor-phase transition-metal catalyzed CTH reaction system of a model oxygenate, viz. acrolein, which is the smallest molecule having both C-C and C-O unsaturation; as such, it can be considered a model representative of biomass-derived molecules and functionalized intermediates in the chemical industry. Designing the acrolein CTH reaction system ultimately requires identifying the optimal donor-catalyst combination that maximizes the yield of a desired product, e.g., hydrogenation selectivity of acrolein to propanal versus propenol. To this end, a novel computational framework that integrates density functional theory (DFT), informatics, machine learning, and several other process systems engineering computational methods including nonlinear optimization and advanced data sampling via reinforcement and transfer learning, will be developed as part of this research to (i) build Gaussian Process surrogate models, (ii) formulate and solve coverage-cognizant microkinetic models, and (iii) solve reaction system optimization problems. This framework will allow the PI to address a critical gap in the fundamental mechanistic elucidation and multiscale design of acrolein CTH reaction systems and thereby identify the optimal donor-catalyst combination from a representative subset of donors and transition metal catalysts. A well-integrated educational program will be developed to target different age groups at Lehigh University and the broader Lehigh valley. This includes engaging high-school and undergraduate students in cutting-edge research at the intersection of data science and catalysis, developing online interactive visualization-based modules to explain high-school science and undergraduate engineering concepts via enquiry-based learning, and developing and offering an interdisciplinary elective to train chemical engineers in the burgeoning area of data science and machine learning.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.