Data-Driven Design of Classes of Ruthenium Nanoparticles Using Multitarget Bayesian Inference-Reference-Cited by-同舟云学术

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Data-Driven Design of Classes of Ruthenium Nanoparticles Using Multitarget Bayesian Inference

Published:2023-01-09 Issue:2 Volume:35 Page:728-738
ISSN:0897-4756
Container-title:Chemistry of Materials
language:en
Short-container-title:Chem. Mater.

Author:

Ting Jonathan Y. C.¹^ORCID,Parker Amanda J.¹^ORCID,Barnard Amanda S.¹^ORCID

Affiliation:

1. School of Computing, Australian National University, Acton2601, Australia

Funder

National Computational Infrastructure

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemmater.2c03435

Reference65 articles.

1. Introduction: Nanoparticles in Catalysis

2. Tao, F. F.; Nguyen, L.; Zhang, S. Metal Nanoparticles for Catalysis: Advances and Applications; The Royal Society of Chemistry: 2014; pp 1–5.

3. Nanocatalysis by noble metal nanoparticles: controlled synthesis for the optimization and understanding of activities

4. Electrocatalysis: Understanding platinum migration

5. Synthesis of Silver Nanostructures with Controlled Shapes and Properties

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2. The emergent role of explainable artificial intelligence in the materials sciences;Cell Reports Physical Science;2023-10

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