An explainable machine-learning approach for revealing the complex synthesis path–property relationships of nanomaterials-Reference-Cited by-同舟云学术

An explainable machine-learning approach for revealing the complex synthesis path–property relationships of nanomaterials

Published:2023 Issue:37 Volume:15 Page:15358-15367
ISSN:2040-3364
Container-title:Nanoscale
language:en
Short-container-title:Nanoscale

Author:

Jin Kun¹,Wang Wentao¹^ORCID,Qi Guangpei¹,Peng Xiaohong²,Gao Haonan¹,Zhu Hongjiang¹,He Xin¹,Zou Haixia¹,Yang Lin¹,Yuan Junjie¹,Zhang Liyuan³,Chen Hong⁴^ORCID,Qu Xiangmeng¹^ORCID

Affiliation:

1. Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province and School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China

2. YueYang Central Hospital, YueYang 414000, China

3. School of Petroleum Engineering, State Key Laboratory of Heavy Oil Processing China University of Petroleum (East China), Qingdao, 266580, China

4. Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, Fujian 361005, China

Abstract

We developed an explainable machine learning approach for revealing the synthesis path-property relationships of nanomaterials and constructed a synthetic phase diagram of the fluorescence quantum yield (QY) of GSH-AuNCs.

Funder

National Key Research and Development Program of China

Basic and Applied Basic Research Foundation of Guangdong Province

Fundamental Research Funds for the Central Universities

Natural Science Foundation of Fujian Province

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2023/NR/D3NR02273K

Reference73 articles.