In silico design of microporous polymers for chemical separations and storage

Dylan M. Anstine; David S. Sholl; Joern Ilja Siepmann; Randall Q. Snurr; Alán Aspuru-Guzik; Coray M. Colina

doi:10.1016/j.coche.2022.100795

In silico design of microporous polymers for chemical separations and storage

Dylan M. Anstine, David S. Sholl, Joern Ilja Siepmann, Randall Q. Snurr, Alán Aspuru-Guzik, Coray M. Colina

Chemical and Biological Engineering

Research output: Contribution to journal › Review article › peer-review

8 Scopus citations

Abstract

Polymers of intrinsic microporosity (PIMs) are a family of materials with potential to be effective and scalable solutions for challenging adsorbent and membrane applications. The broad range of repeat unit chemistry, microporous structural features, and polymer processing makes exploration of the expansive PIM design space inefficient via chemical and materials intuition alone. Computational techniques such as molecular simulations and machine learning can provide a leap in capabilities to address this polymer design challenge and will be central to the future development of PIMs. We highlight recent microporous material studies that arrived at key results by employing computational techniques and provide our perspective on the prospects for in silico design and development of PIMs.

Original language	English (US)
Article number	100795
Journal	Current Opinion in Chemical Engineering
Volume	36
DOIs	https://doi.org/10.1016/j.coche.2022.100795
State	Published - Jun 2022

Funding

The authors acknowledge funding provided by the U.S. Department of Energy (DOE), Office of Basic Energy Sciences , Division of Chemical Sciences, Geosciences and Biosciences under award DE-FG02-17ER16362. The authors also thank Dai Tang, Salah Boulfelfel, Zhenzi Yu, Kaihang Shi, Zhao Li, Roshan Patel, and Saumil Chheda for helpful discussions on the topics addressed by this review.

ASJC Scopus subject areas

General Energy

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title = "In silico design of microporous polymers for chemical separations and storage",

abstract = "Polymers of intrinsic microporosity (PIMs) are a family of materials with potential to be effective and scalable solutions for challenging adsorbent and membrane applications. The broad range of repeat unit chemistry, microporous structural features, and polymer processing makes exploration of the expansive PIM design space inefficient via chemical and materials intuition alone. Computational techniques such as molecular simulations and machine learning can provide a leap in capabilities to address this polymer design challenge and will be central to the future development of PIMs. We highlight recent microporous material studies that arrived at key results by employing computational techniques and provide our perspective on the prospects for in silico design and development of PIMs.",

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AU - Anstine, Dylan M.

AU - Sholl, David S.

AU - Siepmann, Joern Ilja

AU - Snurr, Randall Q.

AU - Aspuru-Guzik, Alán

AU - Colina, Coray M.

PY - 2022/6

Y1 - 2022/6

N2 - Polymers of intrinsic microporosity (PIMs) are a family of materials with potential to be effective and scalable solutions for challenging adsorbent and membrane applications. The broad range of repeat unit chemistry, microporous structural features, and polymer processing makes exploration of the expansive PIM design space inefficient via chemical and materials intuition alone. Computational techniques such as molecular simulations and machine learning can provide a leap in capabilities to address this polymer design challenge and will be central to the future development of PIMs. We highlight recent microporous material studies that arrived at key results by employing computational techniques and provide our perspective on the prospects for in silico design and development of PIMs.

AB - Polymers of intrinsic microporosity (PIMs) are a family of materials with potential to be effective and scalable solutions for challenging adsorbent and membrane applications. The broad range of repeat unit chemistry, microporous structural features, and polymer processing makes exploration of the expansive PIM design space inefficient via chemical and materials intuition alone. Computational techniques such as molecular simulations and machine learning can provide a leap in capabilities to address this polymer design challenge and will be central to the future development of PIMs. We highlight recent microporous material studies that arrived at key results by employing computational techniques and provide our perspective on the prospects for in silico design and development of PIMs.

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In silico design of microporous polymers for chemical separations and storage

Abstract

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