@article{dbd2775a59244a05ac526bca890944f5,
title = "Inverse design of nanoporous crystalline reticular materials with deep generative models",
abstract = "Reticular frameworks are crystalline porous materials that form via the self-assembly of molecular building blocks in different topologies, with many having desirable properties for gas storage, separation, catalysis, biomedical applications and so on. The notable variety of building blocks makes reticular chemistry both promising and challenging for prospective materials design. Here we propose an automated nanoporous materials discovery platform powered by a supramolecular variational autoencoder for the generative design of reticular materials. We demonstrate the automated design process with a class of metal–organic framework (MOF) structures and the goal of separating carbon dioxide from natural gas or flue gas. Our model shows high fidelity in capturing MOF structural features. We show that the autoencoder has a promising optimization capability when jointly trained with multiple top adsorbent candidates identified for superior gas separation. MOFs discovered here are strongly competitive against some of the best-performing MOFs/zeolites ever reported.",
author = "Zhenpeng Yao and Benjam{\'i}n S{\'a}nchez-Lengeling and Bobbitt, {N. Scott} and Bucior, {Benjamin J.} and Kumar, {Sai Govind Hari} and Collins, {Sean P.} and Thomas Burns and Woo, {Tom K.} and Farha, {Omar K.} and Snurr, {Randall Q.} and Al{\'a}n Aspuru-Guzik",
note = "Funding Information: Z.Y., N.S.B., B.J.B., S.G.H.K., O.K.F., R.Q.S. and A.A.-G. were supported as part of the Nanoporous Materials Genome Center by the US Department of Energy, Office of Science, Office of Basic Energy Sciences under award number DE-FG02-17ER16362. Funding for T.B., S.P.C. and T.K.W. were provided by NSERC. Computations were made on the supercomputer {\textquoteleft}beluga{\textquoteright} from {\'E}cole de technologie sup{\'e}rieure, managed by Calcul Qu{\'e}bec and Compute Canada. The operation of this supercomputer is funded by the Canada Foundation for Innovation (CFI), the minist{\`e}re de l{\textquoteright}{\'E}conomie, de la science et de l{\textquoteright}innovation du Qu{\'e}bec (MESI) and the Fonds de recherche du Qu{\'e}bec - Nature et technologies (FRQ-NT). This research was supported in part through the computational resources and staff contributions provided for the Quest high-performance computing facility at Northwestern University, which is jointly supported by the Office of the Provost, the Office for Research, and Northwestern University Information Technology. A.A.-G. is a Canadian Institute for Advanced Research (CIFAR) Lebovic Fellow. Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive licence to Springer Nature Limited.",
year = "2021",
month = jan,
doi = "10.1038/s42256-020-00271-1",
language = "English (US)",
volume = "3",
pages = "76--86",
journal = "Nature Machine Intelligence",
issn = "2522-5839",
publisher = "Springer Nature Switzerland AG",
number = "1",
}