  Electrocatalysts of nitrogen reduction reaction (NRR) have attracted
ever-growing attention due to its application for renewable energy alternative
to fossil fuels. However, activation of inert N-N bond requires multiple
complex charge injection which complicates the design of the catalysts. Here
via combining atomic-scale screening and machine learning (ML) methods we
explore the rational design of NRR single-atom catalysts (SACs) supported by
molybdenum disulfide (MoS2). Our work reveals that the activity of NRR SACs is
highly dependent on the number of unpaired d electrons of TM: positive samples
with high activity favoring higher values while negative cases distributing at
lower values, both varying with the doping conditions of the host. We find that
the substitution of sulfur with boron can activate the intrinsic NRR activity
of some TMs such as Ti and V which are otherwise inactive above pristine MoS2.
Importantly, among the various de-scriptors used in ML, the charged state of
adsorbed TM plays a key role in donating electron to pi-anti-bonding orbital of
N2 via the back-donation mechanism. Our work shows a feasible strategy for
rational design of NRR SACs and retrieval of the decisive feature of active
catalysts.
