2022 Annual Meeting

Session: Molecular and Data Science Modeling of Adsorption I

Chair

Gennady Gor, New Jersey Institute of Technology

Co-Chairs

Presentations

03:30 PM

(572a) Active Learning for Efficient Navigation of Adsorption Landscapes in MOFs
Krishnendu Mukherjee, Alexander Dowling, Yamil Colón

03:45 PM

(572b) Adsorption Isotherms of Argon, Nitrogen, Carbon Dioxide, n-Butane, and Water for Pore Characterization of Chromatographic Particles
Chun-Kai Chang, Hsiao-Feng Liu, Joern Siepmann, Carlos Collados Cuadrado, Carola Schlumberger, Matthias Thommes, Mark R. Schure, Stephanie A. Schuster

04:00 PM

(572c) Mofdb: An Accessible Online Database of Computational Adsorption Data for Nanoporous Materials
Kaihang Shi, Scott Bobbitt, Benjamin Bucior, Haoyuan Chen, Nathaniel Tracy-Amoroso, Zhao Li, Yangzesheng Sun, Julia Merlin, Joern Siepmann, Daniel Siderius, Randall Snurr

04:15 PM

(572d) A Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in Uio-66
Siddarth Achar, Jacob Wardzala, Linfeng Zhang, Leonardo Bernasconi, Karl Johnson

04:30 PM

(572e) Machine Learned Disposable Force Fields for Fluid-Solid Simulations: Application to Water Transport in Carbon and Boron-Nitride Nanotubes
Fabian L. Thiemann, Cristoph Schran, Patrick Rowe, Ondrej Marsalek, Erich A. Muller, Angelos Michaelides

04:45 PM

(645e) Molecular Simulation of Compressibility of Water in Carbon Nanopores
Gennady Gor, Jason Ogbebor, Alexei Khalizov

05:00 PM

(572g) Understanding and Virtual Design of Low-Volatility Ionic Liquid Solvents for Spacecraft CO2 Separations
ISAAC Armstrong, Hendrik Heinz

05:15 PM

(572h) Machine Learning Approach for Construction of Fingerprint Kernels for Pore Structure Characterization of Metal-Organic Frameworks
Shivam Parashar, Alexander Neimark

05:30 PM

(572i) High-Throughput Screening of Hypothetical Functionalized-Irmofs for Separation of Alkanes
Sanket Deshmukh, Fangxi Wang, Abhishek Sose, Samrendra Singh

05:45 PM

(572j) Efficiently Exploring the Adsorption Space of Molecules in MOFs Combining the Use of Molecular Simulations, Machine Learning, and IAST
David Sholl, Xiaohan Yu, Sihoon Choi, Andrew Medford