Afficher la notice abrégée

Causal inference machine learning leads original experimental discovery in CdSe/CdS core/shell nanoparticles

hal.structure.identifierShenzhen Institute of Artificial Intelligence and Robotics for Society [AIRS]
dc.contributor.authorLIU, Rulin
hal.structure.identifierDepartment of Electrical and Electronic Engineering
hal.structure.identifierInstitut de Chimie de la Matière Condensée de Bordeaux [ICMCB]
dc.contributor.authorHAO, Junjie
hal.structure.identifierShenzhen Institute of Artificial Intelligence and Robotics for Society [AIRS]
dc.contributor.authorLI, Jiagen
hal.structure.identifierShenzhen Institute of Artificial Intelligence and Robotics for Society [AIRS]
dc.contributor.authorWANG, Shujie
hal.structure.identifierDepartment of Electrical and Electronic Engineering
dc.contributor.authorLIU, Haochen
hal.structure.identifierDepartment of Electrical and Electronic Engineering
dc.contributor.authorZHOU, Ziming
hal.structure.identifierInstitut de Chimie de la Matière Condensée de Bordeaux [ICMCB]
dc.contributor.authorDELVILLE, Marie-Hélène
hal.structure.identifierSchool of Materials Science and Engineering
dc.contributor.authorCHENG, Jiaji
hal.structure.identifierDepartment of Electrical and Electronic Engineering
dc.contributor.authorWANG, Kai
hal.structure.identifierShenzhen Institute of Artificial Intelligence and Robotics for Society [AIRS]
dc.contributor.authorZHU, Xi
dc.date.issued2020
dc.identifier.issn1948-7185
dc.description.abstractEnThe 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.isoen
dc.publisherAmerican Chemical Society
dc.subject.enMorphology
dc.subject.enLigands
dc.subject.enTheoretical and computational chemistry
dc.subject.enTransmission electron microscopy
dc.subject.enNeural networks
dc.title.enCausal inference machine learning leads original experimental discovery in CdSe/CdS core/shell nanoparticles
dc.typeArticle de revue
dc.identifier.doi10.1021/acs.jpclett.0c02115
dc.subject.halChimie/Matériaux
bordeaux.journalJournal of Physical Chemistry Letters
bordeaux.page7232-7238
bordeaux.volume11
bordeaux.peerReviewedoui
hal.identifierhal-02922356
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02922356v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal%20of%20Physical%20Chemistry%20Letters&rft.date=2020&rft.volume=11&rft.spage=7232-7238&rft.epage=7232-7238&rft.eissn=1948-7185&rft.issn=1948-7185&rft.au=LIU,%20Rulin&HAO,%20Junjie&LI,%20Jiagen&WANG,%20Shujie&LIU,%20Haochen&rft.genre=article


Fichier(s) constituant ce document

FichiersTailleFormatVue

Il n'y a pas de fichiers associés à ce document.

Ce document figure dans la(les) collection(s) suivante(s)

Afficher la notice abrégée