Predicting glass transition temperature and melting point of organic compounds <i>via</i> machine learning and molecular embeddings-Reference-Cited by-同舟云学术

Predicting glass transition temperature and melting point of organic compounds via machine learning and molecular embeddings

Published:2022 Issue:3 Volume:2 Page:362-374
ISSN:2634-3606
Container-title:Environmental Science: Atmospheres
language:en
Short-container-title:Environ. Sci.: Atmos.

Author:

Galeazzo Tommaso¹^ORCID,Shiraiwa Manabu¹^ORCID

Affiliation:

1. Department of Chemistry, University of California, Irvine, CA92697, USA

Abstract

We developed tgBoost a machine learning model to predict glass transition temperature (Tg) of organic species considering their molecular structure and functionality for better predictions of the phase state of secondary organic aerosols.

Funder

National Science Foundation

U.S. Department of Energy

Publisher

Royal Society of Chemistry (RSC)

Subject

Pollution,Environmental Chemistry,Chemistry (miscellaneous),Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/EA/D1EA00090J

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