Engineering topics
Budhathoki, Samir
Publications and source records attributed to Budhathoki, Samir.
Machine Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture
Metal organic frameworks (MOFs) are a large class of porous materials and have garnered significant interest due to their large surface areas and their tunable physical and chemical properties. Numerous prior studies have been performed to screen large databases of this material class for promising DAC sorbent materials. These studies have often relied on classical model potentials. While density functional theory (DFT) calculations have been shown to be very accurate for modeling the interaction of CO2 with MOFs, such calculations are too computationally demanding for statistically significant adsorption predictions. To overcome this barrier, we developed methods for training models to achieve DFT-level accuracy for the forces and energies associated with MOF flexibility and CO2 adsorption using machine learned force fields (MLFFs). These methods were parametrized based on DFT calculations of CO2 in a flexible MOF and used to predict MOF structural properties as well as CO2 adsorption in several MOFs.
Machine-Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture
To cope with legacy greenhouse gas emissions and to achieve net-zero emissions by 2050, the U.S. Department of Energy (DOE) is funding efforts to develop direct air capture (DAC), a method for removing CO2 directly from air. Metal organic frameworks (MOFs) have been well studied as DAC sorbent materials due to their tunable structural and compositional properties. Thermodynamic simulations using force fields are often used to provide predictions of a material’s performance in many separations. However, these force fields often make assumptions about bonds and the physics of the adsorption process. A new class of force fields called machine-learned force fields (MLFFs) use machine learning to form quantitative relationships between a material’s chemical structure and the forces and energies predicted by more accurate quantum mechanical calculations, such as dispersion-corrected density functional theory (DFT). In this work, models were developed to achieve DFT-level accuracy for the forces and energies associated with MOF flexibility and CO2 adsorption using MLFFs. These methods were parametrized based on thousands of DFT calculations of CO2 in flexible MOFs and used to predict MOF structural properties as well as CO2 adsorption in several MOFs.
Creation of Polymer Datasets with Targeted Backbones for Screening of High-Performance Membranes for Gas Separation
A simple approach was developed to computationally construct a polymer dataset by combining simplified molecular-input line-entry system (SMILES) strings of a targeted polymer backbone and a variety of molecular fragments. This method was used to create 14 polymer datasets by combining seven polymer backbones and molecules from two large molecular datasets (MOSES and QM9). Polymer backbones that were studied include four polydimethylsiloxane (PDMS) based backbones, poly(ethylene oxide) (PEO), poly(allyl glycidyl ether) (PAGE), and polyphosphazene (PPZ). The generated polymer datasets can be used for various cheminformatics tasks, including high-throughput screening for gas permeability and selectivity. This study utilized machine learning (ML) models to screen the polymers for CO2/CH4 and CO2/N2 gas separation using membranes. Several polymers of interest were identified. Here the results highlight that employing an ML model fitted to polymer selectivities leads to higher accuracy in predicting polymer selectivity compared to using the ratio of predicted permeabilities.
Computational Modeling of Metal Organic Frameworks for Carbon Capture
Computational Modeling of Metal Organic Frameworks for Carbon Capture
Effect of Flexibility in Molecular Simulations of Carbon Dioxide Adsorption and Diffusion in a Cuprous Triazolate Framework
Using fixed atom force fields to model gas adsorption in flexible metal organic frameworks (MOFs) is known to pose difficulties in accurately reproducing and predicting experimental results. This paper studies the difference in accuracy between flexible and fixed atom force fields in reproducing CO 2 adsorption measurements in MAF-2 ([Cu(etz)]∞ (MAF-2, Hetz) 3,5-diethyl-1,2,4-triazole), an NbO-type triazolate scaffold with a bcu cavity system and attached ethyl groups. The flexible force field used to run the hybrid molecular dynamics and grand canonical Monte Carlo calculations were generated using the QuickFF software incorporating van der Waals parameters from the Universal Force Field (UFF) and density derived electrostatic and chemical (DDEC) charges. The fixed atom force field used was composed of UFF van der Waals parameters together with DDEC charges. The calculations were run at 298 K and at pressures of 0.1, 0.3, 0.5, 0.8, and 1 bar. It was observed that for this MOF the rigid force field overpredicted gas adsorption, whereas the flexible force field values closely matched experimental data. In the flexible structure, the freely moving ethyl groups of MAF-2 hindered adsorption, reducing the interaction energy between CO 2 and the N atoms of the triazolate framework as well as reducing the size of the largest cavity diameter. Here, the combination of these factors led to improved prediction of adsorption values with the flexible forcefield as compared to the rigid forcefield, demonstrating the need for modeling MOFs in a way more indicative of their behavior.
CO2 Adsorption Enhancement of MOF-808 via Highly Efficient Amine Incorporation
For presentation at the 2023 NETL Carbon Management Research Project Review Meeting, Pittsburgh, PA, August 28-September 1, 2023.
Computational Evaluation of Flexible Metal-Organic Frameworks for Capture
Poster for the 2023 FECM / NETL Carbon Management Research Project Review Meeting, Pittsburgh, PA, August 28-September 1, 2023.
Creation of Polymer Datasets for Screening of Gas Permeability and Selectivity
The North American Membrane Society (NAMS) 32nd Annual Meeting, Tuscaloosa, AL, May 14-17, 2023
Computational Screening of Metal Organic Frameworks (MOFS) for Carbon Capture
2022 Carbon Management Project Review Meeting, Pittsburgh, PA, August 15-19, 2022
Computational Screening of MOFs for Carbon Capture
2022 Carbon Management Project Review Meeting, Pittsburgh, PA, August 15-19, 2022
Computationally-Aided Design of Amine-Grafted MOFs for Direct Air Capture
2022 Carbon Management Project Review Meeting, Pittsburgh, PA, August 15-19, 2022
NETL Direct Air Capture Integrated Technology Development
Poster for Carbon Management Meeting
Amine-functionalized porous organic polymers for carbon dioxide capture
Recent developments in CO 2 capture using porous organic polymers (POPs) have received accrescent attention due to their sorbent properties such as high CO 2 uptake capacity and selectivity, tunable chemical structure and permanent porosity. POPs are constructed using two and/or three-dimensional organic monomers (building blocks) linked to each other through covalent bonding, creating high porosity. The pore structure in POPs is exceptionally stable, which leads to their cyclable CO 2 adsorption performance. However, POPs generally suffer from low CO 2 uptake and selectivity due to their interaction with CO 2 in physisorption limits (20–40 kJ mol -1 ). Similar to that in other physisorbents, the CO 2 uptake capacity of POPs further decreases under humid conditions. Pursuant to these limitations, amine functionalization in POPs has resulted in enhanced CO 2 uptake performance with improved CO 2 selectivity over non-polar gases such as N 2 . More importantly, several types of amine-functionalized POPs showed that the CO 2 uptake could remain intact under humid conditions such as in post-combustion flue gas. This review article covers recent developments in amine-functionalized porous organic polymers. Three main categories of amine functionalization, such as direct amine synthesis, amine impregnation and amine grafting, were investigated in detail by considering the effect of amines on the sorbent properties and CO 2 capture performance of POPs. The recent findings in amine-functionalized POPs were investigated including porous polymeric networks (PPNs), covalent organic frameworks (COFs), amine linked POPs, hyper-crosslinked polymers (HCPs), conjugated microporous polymers (CMPs), benzimidazole linked polymers (BILPs), porous aromatic frameworks (PAFs) and polymers of intrinsic microporosity (PIMs).
Computational Study of MOFs for Direct Air Capture Using Flexible Force Fields
Computational Study of MOFs for Direct Air Capture Using Flexible Force Fields
Molecular design and fabrication of PIM-1/polyphosphazene blend membranes with high performance for CO 2 /N 2 separation
New polymeric blend membranes for CO 2 separation were synthesized based on insights from molecular dynamics simulation. A molecular-level structure-property relationship in polymers of intrinsic microporosity (PIM) based blend membranes, was investigated in detail computationally. Calculated local density profiles and energy of interaction of the blend membranes, composed of PIM-1 and various polyphosphazenes, showed that using the polyphosphazene with a higher concentration of ether side chains can improve the compatibility with PIM-1. Furthermore, based on the findings of computational studies, blend membranes were experimentally fabricated from PIM-1 and polyphosphazenes with various polyether side chain concentrations. Polyether concentration in polyphosphazenes was correlated with the film properties and gas transport performance of the blend membranes. Blend membranes showed very high CO 2 permeability (3100-5300 barrer) and improved CO 2 /N 2 selectivity (24-28), outperforming all other PIM-based blend membranes reported to date. Moreover, the CO 2 permeability performance of the blend membranes was tested 566 hours under real post-combustion flue gas from a coal-fired power plant, including CO 2 , N 2 , H 2 O, O 2 , SO x and NO x .