Two-dimensional
semiconducting SnS is expected to have great potential
for application in nanoelectronics. By using both ab initio electronic
structure calculations and more reliable quantum transport simulations,
we systematically explored the interfacial properties of monolayer
(ML) SnS in contact with a series of metals (Ag, Al, Au, Pd, Cu, and
Ni) for the first time. According to the adsorption level, three categories
are found: strong adsorption is found in ML SnS–Pd and Ni contacts;
medium adsorption is found in ML SnS–Cu contacts; and weak
adsorption is found in ML SnS–Ag, Al, and Au contacts. Because
the band structure of ML SnS is destroyed in all of the contact systems,
a vertical Schottky barrier at the ML SnS–metal interface is
absent. However, at the metalized-SnS/uncontacted-SnS interface in
a transistor configuration, a lateral Schottky contact is always formed
as a result of strong Fermi level pinning (with a pinning factor of
0.17–0.28) according to the quantum transport simulations.
This work provides guidelines to design ML SnS-based devices with
optimized electrode contact for high performance.
a b s t r a c tRecently, machine learning (ML) has become a widely used technique in materials science study. Most work focuses on predicting the rule and overall trend by building a machine learning model. However, new insights are often learnt from exceptions against the overall trend. In this work, we demonstrate that how unusual structures are discovered from exceptions when machine learning is used to get the relationship between atomic and electronic structures based on big data from high-throughput calculation database. For example, after training an ML model for the relationship between atomic and electronic structures of crystals, we find AgO 2 F, an unusual structure with both Ag 3+ and O 2 2À , from structures whose band gap deviates much from the prediction made by our model. A further investigation on this structure might shed light into the research on anionic redox in transition metal oxides of Li-ion batteries.
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