MOFormer: Self-Supervised Transformer Model for Metal–Organic Framework Property Prediction-Reference-Cited by-同舟云学术

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MOFormer: Self-Supervised Transformer Model for Metal–Organic Framework Property Prediction

Published:2023-01-27 Issue:5 Volume:145 Page:2958-2967
ISSN:0002-7863
Container-title:Journal of the American Chemical Society
language:en
Short-container-title:J. Am. Chem. Soc.

Author:

Cao Zhonglin¹^ORCID,Magar Rishikesh¹^ORCID,Wang Yuyang¹^ORCID,Barati Farimani Amir¹²³^ORCID

Affiliation:

1. Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States

2. Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States

3. Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States

Funder

Carnegie Mellon University

Publisher

American Chemical Society (ACS)

Subject

Colloid and Surface Chemistry,Biochemistry,General Chemistry,Catalysis

Link

https://pubs.acs.org/doi/pdf/10.1021/jacs.2c11420

Reference66 articles.

1. Metal-organic frameworks

2. Introduction to Metal–Organic Frameworks

3. Exceptional hydrogen storage achieved by screening nearly half a million metal-organic frameworks

4. Data-driven design of metal–organic frameworks for wet flue gas CO2 capture

5. Large-scale screening of hypothetical metal–organic frameworks

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