  Inspired by the neuro-synaptic frameworks in the human brain, neuromorphic
computing is expected to overcome the bottleneck of traditional von-Neumann
architecture and be used in artificial intelligence. Here, we predict a class
of potential candidate materials, MCu$_3$X$_4$ (M = V, Nb, Ta; X = S, Se, Te),
for neuromorphic computing applications through first-principles calculations
based on density functional theory. We find that when MCu$_3$X$_4$ are inserted
with Li atom, the systems would transform from semiconductors to metals due to
the considerable electron filling [~0.8 electrons per formula unit (f.u.)] and
still maintain well structural stability. Meanwhile, the inserted Li atom also
has a low diffusion barrier (~0.6 eV/f.u.), which ensures the feasibility to
control the insertion/extraction of Li by gate voltage. These results establish
that the system can achieve the reversible switching between two stable memory
states, i.e., high/low resistance state, indicating that it could potentially
be used to design synaptic transistor to enable neuromorphic computing. Our
work provides inspiration for advancing the search of candidate materials
related to neuromorphic computing from the perspective of theoretical
calculations.
