TY - JOUR
T1 - From Process to Properties
T2 - Correlating Synthesis Conditions and Structural Disorder of Platinum Nanocatalysts
AU - Sun, Baichuan
AU - Barron, Hector
AU - Opletal, George
AU - Barnard, Amanda S.
N1 - Publisher Copyright:
© 2018 American Chemical Society.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85058114582&partnerID=8YFLogxK
U2 - 10.1021/acs.jpcc.8b08386
DO - 10.1021/acs.jpcc.8b08386
M3 - Article
SN - 1932-7447
VL - 122
SP - 28085
EP - 28093
JO - Journal of Physical Chemistry C
JF - Journal of Physical Chemistry C
IS - 49
ER -