Inverse design of nanoporous crystalline reticular materials with deep generative models-Reference-Cited by-同舟云学术

Inverse design of nanoporous crystalline reticular materials with deep generative models

Published:2021-01 Issue:1 Volume:3 Page:76-86
ISSN:2522-5839
Container-title:Nature Machine Intelligence
language:en
Short-container-title:Nat Mach Intell

Author:

Yao Zhenpeng^ORCID,Sánchez-Lengeling Benjamín,Bobbitt N. Scott,Bucior Benjamin J.^ORCID,Kumar Sai Govind Hari,Collins Sean P.,Burns Thomas,Woo Tom K.,Farha Omar K.,Snurr Randall Q.^ORCID,Aspuru-Guzik Alán^ORCID

Funder

DOE | Office of Science

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software

Link

http://www.nature.com/articles/s42256-020-00271-1.pdf

Reference76 articles.

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3. Mason, J. A. et al. Methane storage in flexible metal–organic frameworks with intrinsic thermal management. Nature 527, 357–361 (2015).

4. Chen, K.-J. et al. Synergistic sorbent separation for one-step ethylene purification from a four-component mixture. Science 366, 241–246 (2019).

5. Nugent, P. et al. Porous materials with optimal adsorption thermodynamics and kinetics for CO2 separation. Nature 495, 80–84 (2013).

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