Machine Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture
- NETL Site Support Contractor, National Energy Technology Laboratory
- Braskem America
- NETL
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.
- Research Organization:
- National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)
- Sponsoring Organization:
- USDOE Office of Fossil Energy and Carbon Management (FECM)
- OSTI ID:
- 2438428
- Country of Publication:
- United States
- Language:
- English
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Machine-Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture
Machine-Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture