  Multi-component metal nanoparticles (NPs) are of paramount importance in the
chemical industry, as most processes therein employ heterogeneous catalysts.
While these multi-component systems have been shown to result in higher product
yields, improved selectivities, and greater stability through catalytic
cycling, the structural dynamics of these materials in response to various
stimuli (e.g. temperature, adsorbates, etc.) are not understood with atomistic
resolution. Here, we present a highly accurate equivariant machine-learned
force field (MLFF), constructed from ab initio training data collected using
Bayesian active learning, that is able to reliably simulate PdAu surfaces and
NPs in response to thermal treatment as well as exposure to reactive H$_2$
atmospheres. We thus provide a single model that is able to reliably describe
the full space of geometric and chemical complexity for such a heterogeneous
catalytic system across single crystals, gas-phase interactions, and NPs
reacting with H$_2$, including catalyst degradation and explicit reactivity.
Ultimately, we provide direct atomistic evidence that verifies existing
experimental hypotheses for bimetallic catalyst deactivation under reaction
conditions, namely that Pd preferentially segregates into the Au bulk through
aggressive catalytic cycling and that this degradation is site-selective, as
well as the reactivity for hydrogen exchange as a function of Pd ensemble size.
We demonstrate that understanding of the atomistic evolution of these active
sites is of the utmost importance, as it allows for design and control of
material structure and corresponding performance, which can be vetted in
silico.
