Machine Learning-Directed Navigation of Synthetic Design Space: A Statistical Learning Approach to Controlling the Synthesis of Perovskite Halide Nanoplatelets in the Quantum-Confined Regime-Reference-Cited by-同舟云学术

Machine Learning-Directed Navigation of Synthetic Design Space: A Statistical Learning Approach to Controlling the Synthesis of Perovskite Halide Nanoplatelets in the Quantum-Confined Regime

Published:2019-04-11 Issue:9 Volume:31 Page:3281-3292
ISSN:0897-4756
Container-title:Chemistry of Materials
language:en
Short-container-title:Chem. Mater.

Author:

Braham Erick J.,Cho Junsang,Forlano Kristel M.,Watson David F.¹^ORCID,Arròyave Raymundo,Banerjee Sarbajit^ORCID

Affiliation:

1. Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, New York 14260-3000, United States

Funder

Division of Graduate Education

Division of Materials Research

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemmater.9b00212

Reference63 articles.

1. Synthesis and Characterization of Monodisperse Nanocrystals and Close-Packed Nanocrystal Assemblies

2. Seed-Mediated Growth Approach for Shape-Controlled Synthesis of Spheroidal and Rod-like Gold Nanoparticles Using a Surfactant Template

3. Colloidal nanocrystal synthesis and the organic–inorganic interface

4. Iridium Nanocrystal Synthesis and Surface Coating-Dependent Catalytic Activity

5. Ligand-Mediated Control of Dislocation Dynamics and Resulting Particle Morphology of GdOCl Nanocrystals

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