Multi-fidelity Bayesian optimization of covalent organic frameworks for xenon/krypton separations

Nickolas Gantzler, Aryan Deshwal, Janardhan Rao Doppa and Cory M. Simon

Digital Discovery, 2023, 2, 1937
DOI: 10.1039/D3DD00117B

Lead-optimized design of metal–organic frameworks for trace SF6 capture

Nickolas Gantzler, Jackson Geary, Jacob Harvey, R. Eric Sikma, Raphael A. Reyes, Caith E. McKeown, N. Scott Bobbitt, Jason P. Sammon and Dorina F. Sava Gallis

J. Mater. Chem. A, 2026, 14, 3253
DOI: 10.1039/D5TA06940H

Computational investigation of multifunctional MOFs for adsorption and membrane-based separation of CF4/CH4, CH4/H2, CH4/N2, and N2/H2 mixtures

Hakan Demir and Seda Keskin

Mol. Syst. Des. Eng., 2022, 7, 1707
DOI: 10.1039/D2ME00130F

Rapid adsorption enthalpy surface sampling (RAESS) to characterize nanoporous materials

Emmanuel Ren and François-Xavier Coudert

Chem. Sci., 2023, 14, 1797
DOI: 10.1039/D2SC05810C

Adaptive Allocation of Monte Carlo Samples for Efficient, Multifidelity Computational Screening of Metal–Organic Frameworks

Qia Ke, N. Scott Bobbitt, Jana Doppa, Huazheng Wang and Cory M. Simon

J. Chem. Theory Comput., 2026, 22, 558
DOI: 10.1021/acs.jctc.5c01431

Screening amorphous materials for separation of Kr/Xe mixture from molecular simulations

Xuan Peng

Molecular Simulation, 2025, 51, 515
DOI: 10.1080/08927022.2025.2505594

Prediction of the Diffusion Coefficient through Machine Learning Based on Transition-State Theory Descriptors

Emmanuel Ren and François-Xavier Coudert

J. Phys. Chem. C, 2024, 128, 6917
DOI: 10.1021/acs.jpcc.4c00631

Alchemical Osmostat for Monte Carlo Simulation: Sampling Aqueous Electrolyte Solution in Open Systems

Ambroise de Izarra, François-Xavier Coudert, Alain H. Fuchs and Anne Boutin

J. Phys. Chem. B, 2023, 127, 766
DOI: 10.1021/acs.jpcb.2c07902

Interface chemistry of conductive crystalline porous thin films

Ming-Shui Yao, Ken-ichi Otake and Susumu Kitagawa

Trends in Chemistry, 2023, 5, 588
DOI: 10.1016/j.trechm.2023.03.002

Enhancing Gas Separation Selectivity Prediction through Geometrical and Chemical Descriptors

Emmanuel Ren and François-Xavier Coudert

Chem. Mater., 2023, 35, 6771
DOI: 10.1021/acs.chemmater.3c01031

Computational Predictions for Effective Separation of Xenon/Krypton Gas Mixtures in the MFM Family of Metal–Organic Frameworks

Isabel Cooley, Louise Efford and Elena Besley

J. Phys. Chem. C, 2022, 126, 11475
DOI: 10.1021/acs.jpcc.2c02237

High-Throughput, Multiscale Computational Screening of Metal–Organic Frameworks for Xe/Kr Separation with Machine-Learned Parameters

Guobin Zhao, Yu Chen and Yongchul G. Chung

Ind. Eng. Chem. Res., 2023, 62, 15176
DOI: 10.1021/acs.iecr.3c02211

Optimizing Isolated Nanotubes for Noble Gas Storage and Separation Applications

Xuan Peng

Langmuir, 2025, 41, 14869
DOI: 10.1021/acs.langmuir.5c00955

Comparison of Realistic and Slit Models of Activated Carbon for Xe/Kr Separation

Xuan Peng

Ind. Eng. Chem. Res., 2025, 64, 8390
DOI: 10.1021/acs.iecr.5c00118