  Tailored Pt nanoparticle catalysts are promising candidates to accelerate the
oxygen reduction reaction (ORR) in fuel cells. However, the search for active
nanoparticle catalysts is hindered by laborious effort of experimental
synthesis and measurements. On the other hand, DFT-based approaches are still
time consuming and often not efficient. In this study, we introduce a
computational model which enables rapid catalytic activity calculation of
unstrained pure Pt nanoparticle electrocatalysts. The generic setup of the
computational model is based on DFT results and experimental data obtained
worldwide over the past ca 20 years; whereas, importantly, the computational
model dispenses with DFT calculations during runtime. This realizes feasible
and sharply reduced computation effort in comparison to theoretical approaches
where DFT calculations must be performed for each nanoparticle individually.
Regarding particle size effects on Pt nanoparticles, experimental catalytic
mass activities from previous studies are accurately reproduced by our
computational model. Shedding light on the parameter space of particle size
effects, this study enables predictions beyond available experiments: Our
computational model identifies potential enhancement in mass activity up to
190% over the experimentally detected maximum. Importantly, the rapid activity
calculation enabled by our computational model may pave the way for extensive
nanoparticle screening to expedite the search for improved electrocatalysts.
