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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Machine-Learned Interatomic Potentials for High-Throughput Screening of Ag-Exchanged Zeolites for Xenon Capture in Nuclear Non-Proliferation Sensing
Author(s) Soham Ketan Savarkar, Eric Fonseca, Richard Hennig
On-Site Speaker (Planned) Soham Ketan Savarkar
Abstract Scope Effective monitoring of underground nuclear activity relies on rapid, reliable capture of escaping radionuclide noble gases, particularly xenon, yet no systematic map exists linking zeolite framework topology to the thermodynamic and kinetic properties needed for trace Xe capture at ambient conditions. We address this gap with a high-throughput computational pipeline using machine-learned interatomic potentials to screen over 4,000 Ag-exchanged zeolite configurations derived from 216 distinct framework topologies. Optimal Ag charge-compensation sites were identified using a conjugate-gradient minimization scheme over candidate atomic placements. We deployed massively parallel, finite-temperature molecular dynamics to evaluate adsorption thermodynamics, Xe binding stability, and transport kinetics across thousands of nanosecond-scale trajectories. This screen isolated target structures, balancing large ring cavities with narrow side channels, combining strong Xe affinity (binding energies above 0.55 eV) with highly localized retention at 300 K (diffusivity < 10^-8 cm/s). We present these candidates alongside a transferable pipeline for gas separation materials design.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Modeling and Simulation, Computational Materials Science & Engineering

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