Chemistry

A robotic platform for the synthesis of colloidal nanocrystals

H. Zhao, W. Chen, et al.

This innovative research introduces a robotic platform that revolutionizes the synthesis of colloidal nanocrystals. By integrating data mining, automated synthesis, and machine learning, the team demonstrates how to control nanocrystal morphologies effectively. This breakthrough opens exciting new pathways for data-driven synthesis in materials science, conducted by the talented authors from the Shenzhen Institute of Advanced Technology and other prestigious institutions.

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Index

Abstract

This paper introduces a robotic platform designed to synthesize colloidal nanocrystals with controlled morphologies, addressing the limitations of traditional trial-and-error methods. Gold and double-perovskite nanocrystals are used as proof-of-concept materials. The platform integrates data mining of synthesis parameters from literature, automated synthesis and in situ characterization, and machine learning for inverse design. The platform successfully demonstrates controllable synthesis and morphology-oriented inverse design, opening avenues for data-driven nanocrystal synthesis.

Publisher

Nature Synthesis

Published On

Jun 01, 2023

Authors

Haitao Zhao, Wei Chen, Hao Huang, Zhehao Sun, Zijian Chen, Lingjun Wu, Baicheng Zhang, Fuming Lai, Zhuo Wang, Mukhtar Lawan Adam, Cheng Heng Pang, Paul K. Chu, Yang Lu, Tao Wu, Jun Jiang, Zongyou Yin, Xue-Feng Yu

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