Causal inference machine learning leads original experimental discovery in CdSe/CdS core/shell nanoparticles
hal.structure.identifier | Shenzhen Institute of Artificial Intelligence and Robotics for Society [AIRS] | |
dc.contributor.author | LIU, Rulin | |
hal.structure.identifier | Department of Electrical and Electronic Engineering | |
hal.structure.identifier | Institut de Chimie de la Matière Condensée de Bordeaux [ICMCB] | |
dc.contributor.author | HAO, Junjie | |
hal.structure.identifier | Shenzhen Institute of Artificial Intelligence and Robotics for Society [AIRS] | |
dc.contributor.author | LI, Jiagen | |
hal.structure.identifier | Shenzhen Institute of Artificial Intelligence and Robotics for Society [AIRS] | |
dc.contributor.author | WANG, Shujie | |
hal.structure.identifier | Department of Electrical and Electronic Engineering | |
dc.contributor.author | LIU, Haochen | |
hal.structure.identifier | Department of Electrical and Electronic Engineering | |
dc.contributor.author | ZHOU, Ziming | |
hal.structure.identifier | Institut de Chimie de la Matière Condensée de Bordeaux [ICMCB] | |
dc.contributor.author | DELVILLE, Marie-Hélène | |
hal.structure.identifier | School of Materials Science and Engineering | |
dc.contributor.author | CHENG, Jiaji | |
hal.structure.identifier | Department of Electrical and Electronic Engineering | |
dc.contributor.author | WANG, Kai | |
hal.structure.identifier | Shenzhen Institute of Artificial Intelligence and Robotics for Society [AIRS] | |
dc.contributor.author | ZHU, Xi | |
dc.date.issued | 2020 | |
dc.identifier.issn | 1948-7185 | |
dc.description.abstractEn | 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. | |
dc.language.iso | en | |
dc.publisher | American Chemical Society | |
dc.subject.en | Morphology | |
dc.subject.en | Ligands | |
dc.subject.en | Theoretical and computational chemistry | |
dc.subject.en | Transmission electron microscopy | |
dc.subject.en | Neural networks | |
dc.title.en | Causal inference machine learning leads original experimental discovery in CdSe/CdS core/shell nanoparticles | |
dc.type | Article de revue | |
dc.identifier.doi | 10.1021/acs.jpclett.0c02115 | |
dc.subject.hal | Chimie/Matériaux | |
bordeaux.journal | Journal of Physical Chemistry Letters | |
bordeaux.page | 7232-7238 | |
bordeaux.volume | 11 | |
bordeaux.peerReviewed | oui | |
hal.identifier | hal-02922356 | |
hal.version | 1 | |
hal.popular | non | |
hal.audience | Internationale | |
hal.origin.link | https://hal.archives-ouvertes.fr//hal-02922356v1 | |
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