Multiscale modeling of nanoporous materials for adsorptive separations

The detrimental effects of rising CO₂ levels on the global climate have made carbon abatement technologies one of the most widely researched areas of recent times. In this thesis, we first present a techno-economic analysis of a novel approach to directly capture CO₂ from air (Air Capture) using highly selective adsorbents. Our process modeling calculations suggest that the monetary cost of Air Capture can be reduced significantly by identifying adsorbents that have high capacities and optimum heats of adsorption. The search for the best performing material is not limited to Air Capture, but is generally applicable for any adsorption-based separation. Recently, a new class of nanoporous materials, Metal-Organic Frameworks (MOFs), have been widely studied using both experimental and computational techniques. In this thesis, we use a combined quantum chemistry and classical simulations approach to predict macroscopic properties of MOFs. Specifically, we describe a systematic procedure for developing classical force fields that accurately represent hydrocarbon interactions with the MIL-series of MOFs using Density Functional Theory (DFT) calculations. We show that this force field development technique is easily extended for screening a large number of complex open metal site MOFs for various olefin/paraffin separations. Finally, we demonstrate the capability of DFT for predicting MOF topologies by studying the effect of ligand functionalization during CuBTC synthesis. This thesis highlights the versatility and opportunities of using multiscale modeling approach that combines process modeling, classical simulations and quantum chemistry calculations to study nanoporous materials for adsorptive separations.Ph.D

Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials

Machine learning potentials (MLPs) capable of accurately describing complex ab initio potential energy surfaces (PESs) have revolutionized the field of multiscale atomistic modeling. In this work, using an extensive density functional theory (DFT) data set (denoted as Si-ZEO22) consisting of 219 unique zeolite topologies (350,000 unique DFT calculations) found in the International Zeolite Association (IZA) database, we have trained a DeePMD-kit MLP to model the dynamics of silica frameworks. The performance of our model is evaluated by calculating various properties that probe the accuracy of the energy and force predictions. This MLP demonstrates impressive agreement with DFT for predicting zeolite structural properties, energy-volume trends, and phonon density of states. Furthermore, our model achieves reasonable predictions for stress-strain relationships without including DFT stress data during training. These results highlight the ability of MLPs to capture the flexibility of zeolite frameworks and motivate further MLP development for nanoporous materials with near-ab initio accuracy

Developing Cheap but Useful Machine Learning-Based Models for Investigating High-Entropy Alloy Catalysts

This work aims to address the challenge of developing interpretable ML-based models when access to large-scale computational resources is limited. Using CoMoFeNiCu high-entropy alloy catalysts as an example, we present a cost-effective workflow that synergistically combines descriptor-based approaches, machine learning-based force fields, and low-cost density functional theory (DFT) calculations to predict high-quality adsorption energies for H, N, and NHx (x = 1, 2, and 3) adsorbates. This is achieved using three specific modifications to typical DFT workflows including: (1) using a sequential optimization protocol, (2) developing a new geometry-based descriptor, and (3) repurposing the already-available low-cost DFT optimization trajectories to develop a ML-FF. Taken together, this study illustrates how cost-effective DFT calculations and appropriately designed descriptors can be used to develop cheap but useful models for predicting high-quality adsorption energies at significantly lower computational costs. We anticipate that this resource-efficient philosophy may be broadly relevant to the larger surface catalysis community

Screening of Copper Open Metal Site MOFs for Olefin/Paraffin Separations Using DFT-Derived Force Fields

Efficient processes for adsorptive separation of light olefin/paraffin mixtures are likely to have many advantages over traditional separation techniques for these commodity chemicals. Although some metal organic frameworks (MOFs) have been studied experimentally for this process, a large-scale computational screening study has not been reported due to the inherent difficulty in describing the critical role of interactions of olefins with open metal sites (OMS). In this paper, we introduce new density functional theory (DFT) derived force fields (FFs) that accurately describe adsorption of C₂ and C₃ olefins and paraffins in CuBTC. Using detailed DFT calculations for MOF-505 and PCN-16, we show that the energetics predicted by our FFs are transferable to other related MOFs that contain Cu OMS. Next, we evaluate the performance of 94 distinct Cu–OMS MOFs for the industrially important propylene/propane separation and identify 18 MOFs predicted to have attractive properties as adsorbents. Finally, we show that the ideal adsorbed solution theory is inaccurate for inhomogeneous olefin/MOF systems and present extensive binary propane/propylene adsorption isotherms for the top-performing MOFs identified in our calculations

DFT-Derived Force Fields for Modeling Hydrocarbon Adsorption in MIL-47(V)

Generic force fields such as UFF and DREIDING are widely used for predicting molecular adsorption and diffusion in metal–organic frameworks (MOFs), but the accuracy of these force fields is unclear. We describe a general framework for developing transferable force fields for modeling the adsorption of alkanes in a nonflexible MIL-47­(V) MOF using periodic density functional theory (DFT) calculations. By calculating the interaction energies for a large number of energetically favorable adsorbate configurations using DFT, we obtain a force field that gives good predictions of adsorption isotherms, heats of adsorption, and diffusion properties for a wide range of alkanes and alkenes in MIL-47­(V). The force field is shown to be transferable to related materials such as MIL-53­(Cr) and is used to calculate the free-energy differences for the experimentally observed phases of MIL-53­(Fe)

Analysis of Equilibrium-Based TSA Processes for Direct Capture of CO₂ from Air

Direct capture of CO₂ from air is a concept that, if successfully implemented, could lead to capture of CO₂ from disperse sources. We have developed process models to consider the viability of adsorption-based air capture technologies. Our models focus on using an amino-modified silica adsorbent, TRI-PE-MCM-41, and a structured monolithic contactor unit. We have studied several different temperature swing adsorption processes using the purity of CO₂ and annual product throughput as metrics for comparing process performance. This analysis identifies some of the operational parameters, adsorbent characteristics, and other factors that have a significant effect on the performance of the process. Using the total energy requirement of the process and available sources of energy, such as low pressure steam and electricity, we carry out an economic analysis to obtain a net operating cost for air capture of CO₂. We identify a process with a daily throughput of ∼1.1 t CO₂ at 88.5% purity using standard shipping container sized air capture units. The total energy required (6745 MJ/t CO₂) is dominated by the parasitic lossessensible heat requirements of the contactor (40%) and the adsorbent (28%) and not by the mechanical energy associated with air flow (∼5%). On the basis of our analysis of factors such as source of electricity, availability of low pressure steam, and geographic location, the net operating cost of capture is estimated to be ∼$100/t CO₂. These cost estimates do not include capital expenses necessary to construct or maintain the air capture units. Potential strategies for further reducing the energy and monetary cost of these processes are identified. Our analysis supports continued work to establish the technological and economic feasibility of adsorption-based air capture

Bridging the Gap between the X-ray Absorption Spectroscopy and the Computational Catalysis Communities in Heterogeneous Catalysis: A Perspective on the Current and Future Research Directions

X-ray absorption spectroscopy (XAS), (Extended X-ray Absorption Fine Structure (EXAFS) and X-ray Absorption Near-Edge Structure (XANES)), is a key technique within the heterogeneous catalysis community to probe the structure and properties of active site(s) for a diverse range of catalytic materials. However, the interpretation of the raw experimental data to derive an atomistic picture of the catalyst requires modeling and analysis; the EXAFS data are compared to a model and a goodness of fit parameter is used to judge the best fit. This EXAFS modeling can often be non-trivial and time-consuming; overcoming or improving these limitations remains a central challenge for the community. Considering these limitations, this Perspective highlights how recent developments in analysis software, increased availability of reliable computational models and application of data science tools can be used to improve the speed, accuracy, and reliability of EXAFS interpretation. In particular, we emphasize the advantages of combining theory and EXAFS as a unified technique that should be treated as a standard (when applicable) to identify catalytic sites and not two separate complementary methods. Building on the recent trends in the computational catalysis community, we also present a community-driven approach to adopt FAIR Guiding Principles for the collection, analysis, dissemination, and storage of XAS data. Written with both the experimental and theory audience in mind, we provide unified roadmap to foster collaborations between the two communities

Theoretical Approaches to Describing the Oxygen Reduction Reaction Activity of Single-Atom Catalysts

Single-atom catalysts have recently emerged as promising low-cost alternatives to Pt for the oxygen reduction reaction (ORR). Given the unique properties that distinguish these systems from traditional transition-metal electrocatalysts, it is essential to benchmark and establish appropriate computational approaches to study these novel materials. Herein, we employ multiple levels of theory, including wave function methods, density functional theory (DFT), and classical simulations, to investigate Cu-modified covalent triazine framework catalysts (Cu/CTF). We consider three major aspects of treating this system computationally. First, we present a step-wise approach to predict the ORR mechanism and adsorbate coverages on Cu/CTF. We then benchmark various DFT methods to coupled-cluster theory with the domain-based local pair natural orbital approximation, which indicates that HSE06 and PBE0 hybrid functionals most accurately describe the adsorption energies of ORR adsorbates on Cu/CTF. We finally employ thermodynamic integration and other techniques to consider solvation effects, which play significant roles in predicting the energies of reaction intermediates and the overall ORR pathway. Our findings indicate that accurate descriptions of both the electronic structure and solvation are necessary to understand the ORR activity of Cu/CTF

Screening Cu-Zeolites for Methane Activation Using Curriculum-Based Training

Machine learning (ML), when used synergistically with atomistic simulations, has recently emerged as a powerful tool for accelerated catalyst discovery. However, the application of these techniques has been limited by the lack of interpretable and transferable ML models. In this work, we propose a curriculum-based training (CBT) philosophy to systematically develop reactive machine learning potentials (rMLPs) for high-throughput screening of zeolite catalysts. Our CBT approach combines several different types of calculations to gradually teach the ML model about the relevant regions of the reactive potential energy surface. The resulting rMLPs are accurate, transferable, and interpretable. We further demonstrate the effectiveness of this approach by exhaustively screening thousands of [CuOCu]2+ sites across hundreds of Cu-zeolites for the industrially relevant methane activation reaction. Specifically, this large-scale analysis of the entire International Zeolite Association (IZA) database identifies a set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS) that show the highest ensemble-averaged rates for [CuOCu]2+-catalyzed methane activation. We believe that this CBT philosophy can be generally applied to other zeolite-catalyzed reactions and, subsequently, to other types of heterogeneous catalysts. Thus, this represents an important step toward overcoming the long-standing barriers within the computational heterogeneous catalysis community