Predicting the chemical reactivity of organic materials using a machine-learning approach-Reference-Cited by-同舟云学术

Predicting the chemical reactivity of organic materials using a machine-learning approach

Published:2020 Issue:30 Volume:11 Page:7813-7822
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Lee Byungju¹²³⁴⁵,Yoo Jaekyun¹²³⁴⁵,Kang Kisuk¹²³⁴⁵^ORCID

Affiliation:

1. Research Institute of Advanced Materials (RIAM)

2. Department of Materials Science and Engineering

3. Seoul National University

4. Seoul 151-742

5. Republic of Korea

Abstract

Stability and compatibility between chemical components are essential parameters that need to be considered in the selection of functional materials in configuring a system.

Funder

National Research Foundation of Korea

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2020/SC/D0SC01328E

Reference67 articles.

1. Rational design of redox mediators for advanced Li–O2 batteries

2. Solar cell efficiency tables (Version 45)

3. Photovoltaic solar cell technologies: analysing the state of the art

4. Reaction chemistry in rechargeable Li–O2batteries

5. High-efficiency dye-sensitized solar cells with ferrocene-based electrolytes

Cited by 35 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. Machine learning-accelerated discovery and design of electrode materials and electrolytes for lithium ion batteries;Energy Storage Materials;2024-09

2. Two new Cr(III) (C1) and Fe(III) (C2) complexes incorporating 2,6-pyridinedicarboxylic acid (PDC) and 2-methyl-1H-benzimidazole (MB): Synthesis, structure, DFT and phenoxazinone synthase mimicking activity;Journal of Molecular Structure;2024-08

3. A machine learning approach for predicting the reactivity power of hypervalent iodine compounds;Artificial Intelligence Chemistry;2024-06

4. Bio-based silsesquioxanes nano hybrids for enhancing the tribological performance of silicone oil: A DFT and experimental study;Journal of Industrial and Engineering Chemistry;2024-06

5. Machine learning in metal-ion battery research: Advancing material prediction, characterization, and status evaluation;Journal of Energy Chemistry;2024-03