From Process to Properties: Correlating Synthesis Conditions and Structural Disorder of Platinum Nanocatalysts

Baichuan Sun*, Hector Barron, George Opletal, Amanda S. Barnard

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

Understanding the complicated relationship between various synthetic processing parameters and the functional properties or performance of nanoparticles is one of the goals of computational materials design, and is an ideal problem for materials informatics. In this study, we use machine learning to build predictions of how bulk and surface disorder is controlled by the growth time, atom deposition rate, and temperature, and how they can impact some indicators of the functional properties of platinum nanoparticles used in catalysis, including a range of structural features. We have used an ensemble of 690 unique nanoparticles generated from molecular dynamics trajectories that sample a large variety of different temperature/growth rate combinations, and genetic algorithms providing a prediction accuracy over 85% for all models. We have developed reliable and insightful predictions of structure/property and process/structure relationships (even when the polydispersed structures are almost entirely disordered), but our results suggest that the transcendent process/property relationship requires more detailed descriptors than those traditionally available from conventional computational studies.

Original languageEnglish
Pages (from-to)28085-28093
Number of pages9
JournalJournal of Physical Chemistry C
Volume122
Issue number49
DOIs
Publication statusPublished - 2018
Externally publishedYes

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