most citedDecoding local framework dynamics in the ultra-small pore MOF MIL-120(Al) CO2 sorbent with Machine Learned Potentials

1 citations · 1 across the 1 of their papers we have counts for

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2 papers

cond-mat.mtrl-sci2025

Towards Accurate and Scalable High-throughput MOF Adsorption Screening: Merging Classical Force Fields and Universal Machine Learned Interatomic Potentials

Satyanarayana Bonakala, Mohammad Wahiduzzaman, Taku Watanabe +2

High-throughput computational screening (HTCS) of gas adsorption in metal-organic frameworks (MOFs) typically relies on classical generic force fields such as the Universal Force F…

cond-mat.mtrl-sci2025★ 1 cited

Decoding local framework dynamics in the ultra-small pore MOF MIL-120(Al) CO2 sorbent with Machine Learned Potentials

Dong Fan, Felipe Lopes Oliveira, Mohammad Wahiduzzaman +1

Metal-organic frameworks (MOFs) with ultra-small pores offer an optimal environment to effectively capture guest molecules such as CO2. Subtle local dynamics of their frameworks, e…