From synthesis to properties: expanding the horizons of machine learning in nanomaterials research.

Shanhui Diao^1,2, Qiong Wu^1,2, Shimei Li^1,2, Guochen Xu^1,2, Xiangling Ren^1,2, Longfei Tan^1,2, Guihua Jiang³, Peng Song⁴, Xianwei Meng^1,2

¹State Key Laboratory of Cryogenic Science and Technology, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China.

²Laboratory of Controllable Preparation and Application of Nanomaterials, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China. wq19931110@mail.ipc.ac.cn.

³The Department of Medical Imaging, The Affiliated Guangdong Second Provincial General Hospital of Jinan University Guangzhou 518037, China. jianggh@gd2h.org.cn.

⁴Department of MedicaI Oncology, The Second Medical Center and NationaI cIinicaI Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China.

PubMed DOI

Abstract

Nanotechnology involves the manipulation of matter at dimensions ranging from 1 to 100 nanometers. Prediction of synthesis parameters, structure, properties and applications is a cascade process in nanomaterials research, each of these stages being interconnected and having a correlative influence on one another. Traditionally, the "trial and error" approach in nanomaterial research has several li…

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