2024 AIChE Annual Meeting

Session: Molecular and Data Science Modeling of Adsorption

This session focuses on the studies of adsorption in materials via molecular simulations and/or through data-science approaches.

Chair

Gor, G., New Jersey Institute of Technology

Co-Chair

Corrente, N., Rutgers University

Presentations

12:30 PM

(556a) Physics-Based Universal Representation of Single and Multi-Component Equilibria to Simulate Complex Adsorption Processes

12:45 PM

(381x) Self-Diffusion of Volatile Organic Compounds in Zr-MOFs: A Joint Computational and Experimental Study

01:00 PM

(556c) Data-Driven Screening of Metal-Organic Frameworks for Selective C2 Separations

01:15 PM

(556d) Using Data Mining and Large Language Models for a Large-Scale Comparison of Adsorption from Molecular Simulation and Experiment

01:30 PM

(556e) Integrating Stability with High-Throughput Screening of Metal-Organic Frameworks for CO2 Capture in Wet Flue Gas: A Combination of Machine Learning and Molecular Simulation

01:45 PM

(556f) Machine Learning Approach to Predict Adsorption Properties in Cofs

02:05 PM

(556g) Molecular Simulations Probing the Adsorption and Diffusion of Ammonia, Nitrogen, Hydrogen, and Their Mixtures in Bulk MFI Zeolite and MFI Nanosheets

02:20 PM

(556h) Using Trajectory-Extending Kinetic Monte Carlo Simulations to Reassess Membrane Selectivities for Gas Mixtures in Carbon Molecule Sieves