Causal inference machine learning leads original experimental discovery in CdSe/CdS core/shell nanoparticles
HAO, Junjie
Department of Electrical and Electronic Engineering
Institut de Chimie de la Matière Condensée de Bordeaux [ICMCB]
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Department of Electrical and Electronic Engineering
Institut de Chimie de la Matière Condensée de Bordeaux [ICMCB]
HAO, Junjie
Department of Electrical and Electronic Engineering
Institut de Chimie de la Matière Condensée de Bordeaux [ICMCB]
< Reduce
Department of Electrical and Electronic Engineering
Institut de Chimie de la Matière Condensée de Bordeaux [ICMCB]
Language
en
Article de revue
This item was published in
Journal of Physical Chemistry Letters. 2020, vol. 11, p. 7232-7238
American Chemical Society
English Abstract
The synthesis of CdSe/CdS core/shell nanoparticles was revisited with the help of a causal inference machine learning framework. The tadpole morphology with 1–2 tails was experimentally discovered. The causal inference ...Read more >
The synthesis of CdSe/CdS core/shell nanoparticles was revisited with the help of a causal inference machine learning framework. The tadpole morphology with 1–2 tails was experimentally discovered. The causal inference model revealed the causality between the oleic acid (OA), octadecylphosphonic acid (ODPA) ligands, and the detailed tail shape of the tadpole morphology. Further, with the identified causality, a neural network was provided to predict and directly lead to the original experimental discovery of new tadpole-shaped structures. An entropy-driven nucleation theory was developed to understand both the ligand and temperature dependent experimental data and the causal inference from the machine learning framework. This work provided a vivid example of how the artificial intelligence technology, including machine learning, could benefit the materials science research for the discovery.Read less <
English Keywords
Morphology
Ligands
Theoretical and computational chemistry
Transmission electron microscopy
Neural networks
Origin
Hal imported