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N. Scott Bobbitt
Researcher at Northwestern University
Publications - 25
Citations - 1257
N. Scott Bobbitt is an academic researcher from Northwestern University. The author has contributed to research in topics: Density functional theory & Hydrogen storage. The author has an hindex of 8, co-authored 21 publications receiving 852 citations. Previous affiliations of N. Scott Bobbitt include Sandia National Laboratories & University of Arkansas.
Papers
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Journal ArticleDOI
Metal–organic frameworks for the removal of toxic industrial chemicals and chemical warfare agents
N. Scott Bobbitt,Matthew L. Mendonca,Ashlee J. Howarth,Timur Islamoglu,Joseph T. Hupp,Omar K. Farha,Omar K. Farha,Randall Q. Snurr +7 more
TL;DR: This paper reviews recent experimental and computational work pertaining to the capture of several industrially-relevant toxic chemicals, including NH3, SO2, NO2, H2S, and some volatile organic compounds, with particular emphasis on the challenging issue of designing materials that selectively adsorb these chemicals in the presence of water.
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Energy-based descriptors to rapidly predict hydrogen storage in metal–organic frameworks
Benjamin J. Bucior,N. Scott Bobbitt,Timur Islamoglu,Subhadip Goswami,Arun Gopalan,Taner Yildirim,Omar K. Farha,Neda Bagheri,Randall Q. Snurr +8 more
TL;DR: In this article, a data-driven approach was developed to accelerate materials screening and learn structure-property relationships, and new descriptors for gas adsorption in metal-organic frameworks (MOFs) derived from the energetics of MOF-guest interactions.
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Inverse design of nanoporous crystalline reticular materials with deep generative models
Zhenpeng Yao,Zhenpeng Yao,Benjamin Sanchez-Lengeling,N. Scott Bobbitt,Benjamin J. Bucior,Sai Govind Hari Kumar,Sean P. Collins,Thomas Burns,Tom K. Woo,Omar K. Farha,Randall Q. Snurr,Alán Aspuru-Guzik +11 more
TL;DR: In this article, an automated nanoporous materials discovery platform powered by a supramolecular variational autoencoder was proposed for the generative design of reticular materials, which can efficiently explore this space.
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High-Throughput Screening of Metal-Organic Frameworks for Hydrogen Storage at Cryogenic Temperature
TL;DR: In this paper, the authors evaluated 137,953 hypothetical MOFs for hydrogen storage at cryogenic conditions (77 K) by determining the deliverable storage capacity between 100 and 2 bar, using grand canonical Monte Carlo simulations.
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Molecular modelling and machine learning for high-throughput screening of metal-organic frameworks for hydrogen storage
TL;DR: In this article, the authors proposed a hydrogen storage solution for electric vehicles due to its low environmental impact and faster recharge times compared to batteries. But, there are many engineering chall...