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Title: Designing green chemicals by predicting vaporization properties using explainable graph attention networks

Journal Article · · Green Chemistry
DOI: https://doi.org/10.1039/D4GC01994F · OSTI ID:2440103
ORCiD logo [1];  [2]; ORCiD logo [3];  [4];  [4];  [5]; ORCiD logo [5]; ORCiD logo [4]; ORCiD logo [4]; ORCiD logo [6]
  1. Department of Chemistry, Colorado State University, Fort Collins, CO 80523, USA, Department of Chemistry, Pukyong National University, Busan 48513, Republic of Korea
  2. National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USA, Department of Aerospace and Mechanical Engineering, The University of Texas at El Paso, El Paso, TX 79968, USA
  3. Department of Chemistry, Colorado State University, Fort Collins, CO 80523, USA, Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul 03722, Republic of Korea
  4. National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USA
  5. Department of Chemistry, Colorado State University, Fort Collins, CO 80523, USA
  6. Department of Chemistry, Colorado State University, Fort Collins, CO 80523, USA, National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USA

Computational predictions of vaporization properties aid the de novo design of green chemicals, including clean alternative fuels, working fluids for efficient thermal energy recovery, and polymers that are easily degradable and recyclable.

Sponsoring Organization:
USDOE
Grant/Contract Number:
AC36-08GO28308
OSTI ID:
2440103
Journal Information:
Green Chemistry, Journal Name: Green Chemistry Journal Issue: 19 Vol. 26; ISSN GRCHFJ; ISSN 1463-9262
Publisher:
Royal Society of Chemistry (RSC)Copyright Statement
Country of Publication:
United Kingdom
Language:
English

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