Electron configuration-based neural network model to predict physicochemical properties of inorganic compounds-Reference-Cited by-同舟云学术

Electron configuration-based neural network model to predict physicochemical properties of inorganic compounds

Published:2020 Issue:55 Volume:10 Page:33268-33278
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Shin Hyun Kil¹²³⁴⁵^ORCID

Affiliation:

1. Toxicoinformatics Group

2. Department of Predictive Toxicology

3. Korea Institute of Toxicology

4. Daejeon

5. Republic of Korea

Abstract

Registration, evaluation, and authorization of chemicals (REACH), the regulation of chemicals in use, imposes the characterization and report of the physicochemical properties of compounds.

Funder

National Research Foundation of Korea

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2020/RA/D0RA05873D

Reference32 articles.

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2. J.Kim , H. K.Shin , J. S.Choi , S.Yoon and T. H.Yoon , in Information resources in toxicology , ed. S. G. Gilbert , A. Mohapatra , S. Bobst , A. Hayes , S. T. Humes and P. Wexler , Andre Gerhard Wolff, Elsevier , 5th edn, 2020 , ch. 35, vol. 2 , pp. 487–488

3. Reach - an overview

4. Act on the Registration and Evaluation of Chemicals (K-REACH) and replacement, reduction or refinement best practices

5. QSPR prediction of physico-chemical properties for REACH

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