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Trigger Smart Data Saving Applied to CO2 Capture in Metal-Organic Frameworks
DescriptionFacing the need for carbon emission reduction, processes such as CO2 capture in nanoporous Metal-Organic Frameworks (MOFs) have emerged. However, such processes still need to be improved, by understanding the dynamic properties of CO2 molecules when confined in MOF nanopores. To do so, molecular dynamics (MD) simulations are run for several millions of iterations, enabling to accurately compute the CO2 residency time. Nevertheless, this dynamical parameter remains challenging to compute by standard post-processing approaches and may require terabytes of memory when data are saved after each iteration. To tackle this issue, we developed a trigger-based in situ approach that saves only the relevant data. We implement it by instrumenting the LAMMPS MD code with the SENSEI/Python in situ API. We show that this approach reduces the quantity of data saved by 4 orders of magnitude and can be up to 14% faster than traditional MD simulations without in situ processing.
Event Type
Workshop
TimeMonday, 13 November 202311:10am - 11:30am MST
Location506
Data Analysis, Visualization, and Storage
Large Scale Systems
Performance Measurement, Modeling, and Tools
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