The search for sparse data in molecular datasets: Application of active learning to identify extremely low volatile organic compounds-Reference-Cited by-同舟云学术

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The search for sparse data in molecular datasets: Application of active learning to identify extremely low volatile organic compounds

Published:2024-06 Issue: Volume:179 Page:106375
ISSN:0021-8502
Container-title:Journal of Aerosol Science
language:en
Short-container-title:Journal of Aerosol Science

Author:

Besel Vitus^ORCID,Todorović Milica,Kurtén Theo,Vehkamäki Hanna,Rinke Patrick

Funder

Academy of Finland

Publisher

Elsevier BV

Reference39 articles.

1. Technical summary;Arias,2021

2. Modelling the evolution of organic carbon during its gas-phase tropospheric oxidation: development of an explicit model based on a self generating approach;Aumont;Atmospheric Chemistry and Physics,2005

3. TURBOMOLE: Modular program suite for ab initio quantum-chemical and condensed-matter simulations;Balasubramani;Journal of Chemical Physics,2020

4. Density-functional exchange-energy approximation with correct asymptotic behavior;Becke;Physical Review A,1988

5. Atomic structures, conformers and thermodynamic properties of 32k atmospheric molecules;Besel;Scientific Data,2023

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Accurate modeling of the potential energy surface of atmospheric molecular clusters boosted by neural networks;Environmental Science: Advances;2024

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