1. Introduction
Carbon nanostructures are ubiquitous. This versatility comes from the ability of carbon to form bonds through sp, sp
2, and sp
3 hybridization. The different types of hybridization can lead to various allotropes and complexes. Carbon is also available in nature as diamond and graphite. It forms all organic matters and is the basis of life. It is also an essential element in many drugs [
1,
2]. The nanoforms of allotropes were first introduced by the discovery of C
60 molecules [
3,
4], followed by the observation of carbon nanotubes [
5], then followed by the isolation and many groundbreaking experiments in graphene [
6,
7]. All these nanostructures have various special properties that are interesting and can be advantageous for specific applications. The series of carbon nanoallotropes have created a fresh search for new 0D, 1D, and 2D nanomaterials [
2]. Among carbon nanomaterials, graphene, which is a 2D nanomaterial, stands out, due to its fascinating properties and vast potential applications [
7,
8].
As graphene lacks a band gap, its finite nanostructures, such as graphene nanoribbons (GNRs), are also of great interest because of a wide range of possibilities to tune their properties for specific applications. GNRs can be thought of quasi-1D nanostructures carved from graphene. GNRs can exhibit intriguing electronic, optical, and magnetic characteristics [
9,
10,
11,
12,
13,
14,
15,
16], which can be useful for many applications [
16,
17,
18,
19]. In general, GNRs can be classified into two main categories: zigzag GNRs and armchair GNRs, based on their edge patterns [
12,
13,
14]. Both zigzag and armchair GNRs of various widths have been successfully synthesized [
12]. There are also other types of GNRs with different kinds of edge structures, such as the chevron, fjord, chiral, junction, cove, and gulf types of GNRs [
20]. Although there is a rich literature on these electronic systems, ongoing theoretical and experimental studies aim to further explore the properties of GNRs.
Although zigzag and armchair GNRs have been extensively documented in the literature, reports on the parallelogram-shaped GNRs with zigzag edges (see
Figure 1 for illustration), denoted as graphene nano-parallelograms (GNPs), are relatively scarce. In this study, the narrowest GNPs, which are
n-acenes (i.e., acenes with
n linearly fused benzene rings), are labeled as GNP[1,
n]; the second narrowest GNPs are labeled as GNP[2,
n], containing two parallelly fused
n-acenes forming parallelograms; the third narrowest GNPs are labeled as GNP[3,
n], containing three parallelly fused
n-acenes forming parallelograms, and so on. For GNP[
m,
n], the
m value specifies the GNP width, and the
n value specifies the GNP length. Some GNPs, such as GNP[2,4] and GNP[2,5] (also called [
n,2]peri-acenoacenes with
n = 4 and 5, respectively) have been recently synthesized in molecular and crystalline forms [
21,
22]. Besides, for GNP[2,
n] (with
n = 3–5), their properties and lasing applications in the near-infrared (NIR) region have been recently reported [
23]. In addition, a computational study has recently reported a few electronic properties of GNP[
m,
n] (with
= 2–4) [
24]. However, several properties of GNP[
m,
n] with different widths and lengths remain unavailable. Investigating the finite-size effects of GNP[
m,
n] is essential to gain a comprehensive understanding of the width- and length-dependent electronic properties of GNP[
m,
n]. Therefore, this computational study aims at exploring the electronic properties of GNP[
m,
n] with various values of
m and
n. Our previous study has revealed the presence of strong static correlation effects and intriguing properties in the longer zigzag GNRs [
25]. Accordingly, it can be anticipated that GNPs, which have similar shapes as zigzag GNRs, can also possess strong static correlation effects in their ground states (as will be shown later).
Among existing electronic structure methods, Kohn-Sham density functional theory (KS-DFT) [
26,
27] is popular and valuable for investigating the ground-state properties of electronic systems, especially for single-reference (SR) systems (i.e., electronic systems possessing SR character in their ground states). In fact, KS-DFT has served as the backbone of theoretical condensed matter physics, quantum chemistry, and computational materials science [
28]. For the sake of computational efficiency, KS-DFT calculations are commonly carried out using the local density approximation (LDA) [
29,
30] and generalized gradient approximation (GGA) [
31] exchange-correlation (xc) energy functionals. However, KS-DFT with the LDA and GGA xc energy functionals can encounter severe problems for issues related to the self-interaction error, non-covalent interaction error, and static correlation error [
32,
33,
34,
35]. Among these qualitative errors, the self-interaction error can be reduced by the use of hybrid xc energy functionals [
36,
37,
38], incorporating a fraction of Hartree-Fock exchange into the parent LDA or GGA xc energy functionals. Besides, a number of computationally efficient dispersion correction schemes [
39,
40], which can be directly added to the parent LDA or GGA xc energy functionals, are readily available for improving the description of non-covalent interactions. Nevertheless, the presence of strong static correlation effects in multi-reference (MR) systems (i.e., electronic systems possessing MR character in their ground states) has posed a formidable challenge, particularly for KS-DFT with the conventional LDA, GGA, and hybrid xc energy functionals. Generally, SR electronic structure methods (e.g., KS-DFT with the conventional LDA, GGA, and hybrid xc energy functionals as well as the Hartree-Fock theory) are unreliable for studying the ground-state properties of MR systems.
Typically, to explore the ground-state properties of MR systems,
ab initio MR electronic structure methods [
41,
42,
43,
44,
45,
46,
47], such as the MR configuration interaction (MRCI) methods and density-matrix renormalization group (DMRG) algorithms, are essential [
48,
49]. However, these methods can become impractical for large electronic systems due to the prohibitively high cost of performing
ab initio MR electronic structure calculations. Accordingly, the demand for a reliable and efficient electronic structure method for large MR systems is very high.
Recently, thermally-assisted-occupation density functional theory (TAO-DFT) [
50] has emerged as an effective solution for addressing the challenges posed by large MR systems. Generally, TAO-DFT focuses on improving the representability of ground-state electron density by incorporating fractional orbital occupation numbers, which can be efficiently computed using the Fermi-Dirac (FD) distribution function with some fictitious temperature
. The LDA [
50], GGA [
51], global hybrid [
52], and range-separated [
52,
53] exchange-correlation-
(xc
) energy functionals (i.e., the combined xc and
-dependent energy functionals [
54]) can be incorporated in TAO-DFT. Besides, simple models for defining the optimal system-independent [
55] and system-dependent [
56] fictitious temperatures of an energy functional in TAO-DFT have been recently proposed. Note also that the difference among KS-DFT [
26,
27], TAO-DFT [
50], and finite-temperature density functional theory (FT-DFT) [
27,
57] (i.e., three generally different electronic structure methods) has been properly discussed in a recent work [
54].
Very recently, various TAO-DFT-related extensions, such as TAO-DFT-based
ab initio molecular dynamics (TAO-AIMD) [
58], TAO-DFT with the polarizable continuum model (TAO-PCM) [
59], and a real-time extension of TAO-DFT (RT-TAO-DFT) [
54], have been developed, expanding the capabilities of TAO-DFT to handle a more diverse range of applications. Moreover, TAO-DFT has also been adopted to investigate the various properties (e.g., electronic properties [
25,
60,
61,
62,
63,
64,
65,
66,
67,
68], hydrogen storage properties [
61,
63], spectroscopic properties [
58,
69,
70], and equilibrium thermodynamic properties [
58]) of MR systems at the nanoscale.
Consequently, in this computational study, we utilize TAO-DFT to obtain the electronic properties of GNP[m,n] (with m = 1–4 and n = 2–30), including the singlet-triplet energy gaps, vertical electron affinities / ionization potentials, fundamental gaps, symmetrized von Neumann entropy, active orbital occupation numbers, and real-space representation of active orbitals.
Figure 1.
Structures of GNP[m,10] with m = 1 (i.e., 10-acene, containing 10 linearly fused benzene rings) and GNP[m,10] with m = 2–4 (containing m parallelly fused 10-acenes forming parallelograms).
Figure 1.
Structures of GNP[m,10] with m = 1 (i.e., 10-acene, containing 10 linearly fused benzene rings) and GNP[m,10] with m = 2–4 (containing m parallelly fused 10-acenes forming parallelograms).
Figure 2.
Singlet-triplet energy gap of GNP[m,n], calculated using spin-unrestricted TAO-LDA.
Figure 2.
Singlet-triplet energy gap of GNP[m,n], calculated using spin-unrestricted TAO-LDA.
Figure 3.
Vertical ionization potential for the ground state of GNP[m,n], calculated using spin-unrestricted TAO-LDA.
Figure 3.
Vertical ionization potential for the ground state of GNP[m,n], calculated using spin-unrestricted TAO-LDA.
Figure 4.
Vertical electron affinity for the ground state of GNP[m,n], calculated using spin-unrestricted TAO-LDA.
Figure 4.
Vertical electron affinity for the ground state of GNP[m,n], calculated using spin-unrestricted TAO-LDA.
Figure 5.
Fundamental gap for the ground state of GNP[m,n], calculated using spin-unrestricted TAO-LDA.
Figure 5.
Fundamental gap for the ground state of GNP[m,n], calculated using spin-unrestricted TAO-LDA.
Figure 6.
Symmetrized von Neumann entropy for the ground state of GNP[m,n], calculated using spin-unrestricted TAO-LDA.
Figure 6.
Symmetrized von Neumann entropy for the ground state of GNP[m,n], calculated using spin-unrestricted TAO-LDA.
Figure 7.
Occupation numbers of active orbitals (HOMO−9, HOMO−8, ..., HOMO, LUMO, ..., LUMO+8, and LUMO+9) for the ground state of GNP[1,n], calculated using spin-restricted TAO-LDA.
Figure 7.
Occupation numbers of active orbitals (HOMO−9, HOMO−8, ..., HOMO, LUMO, ..., LUMO+8, and LUMO+9) for the ground state of GNP[1,n], calculated using spin-restricted TAO-LDA.
Figure 8.
Occupation numbers of active orbitals (HOMO−9, HOMO−8, ..., HOMO, LUMO, ..., LUMO+8, and LUMO+9) for the ground state of GNP[2,n], calculated using spin-restricted TAO-LDA.
Figure 8.
Occupation numbers of active orbitals (HOMO−9, HOMO−8, ..., HOMO, LUMO, ..., LUMO+8, and LUMO+9) for the ground state of GNP[2,n], calculated using spin-restricted TAO-LDA.
Figure 9.
Occupation numbers of active orbitals (HOMO−9, HOMO−8, ..., HOMO, LUMO, ..., LUMO+8, and LUMO+9) for the ground state of GNP[3,n], calculated using spin-restricted TAO-LDA.
Figure 9.
Occupation numbers of active orbitals (HOMO−9, HOMO−8, ..., HOMO, LUMO, ..., LUMO+8, and LUMO+9) for the ground state of GNP[3,n], calculated using spin-restricted TAO-LDA.
Figure 10.
Occupation numbers of active orbitals (HOMO−9, HOMO−8, ..., HOMO, LUMO, ..., LUMO+8, and LUMO+9) for the ground state of GNP[4,n], calculated using spin-restricted TAO-LDA.
Figure 10.
Occupation numbers of active orbitals (HOMO−9, HOMO−8, ..., HOMO, LUMO, ..., LUMO+8, and LUMO+9) for the ground state of GNP[4,n], calculated using spin-restricted TAO-LDA.
Figure 11.
Real-space representation of active orbitals (HOMO and LUMO) for the ground state of GNP[m,5] with (a) , (b) , (c) , and (d) , at an isovalue of 0.02 e/Å3, calculated using spin-restricted TAO-LDA, where the orbital occupation numbers are shown in parentheses.
Figure 11.
Real-space representation of active orbitals (HOMO and LUMO) for the ground state of GNP[m,5] with (a) , (b) , (c) , and (d) , at an isovalue of 0.02 e/Å3, calculated using spin-restricted TAO-LDA, where the orbital occupation numbers are shown in parentheses.
Figure 12.
Real-space representation of active orbitals (HOMO and LUMO) for the ground state of GNP[m,10] with (a) , (b) , (c) , and (d) , at an isovalue of 0.02 e/Å3, calculated using spin-restricted TAO-LDA, where the orbital occupation numbers are shown in parentheses.
Figure 12.
Real-space representation of active orbitals (HOMO and LUMO) for the ground state of GNP[m,10] with (a) , (b) , (c) , and (d) , at an isovalue of 0.02 e/Å3, calculated using spin-restricted TAO-LDA, where the orbital occupation numbers are shown in parentheses.
Figure 13.
Real-space representation of active orbitals (HOMO and LUMO) for the ground state of GNP[m,15] with (a) , (b) , (c) , and (d) , at an isovalue of 0.02 e/Å3, calculated using spin-restricted TAO-LDA, where the orbital occupation numbers are shown in parentheses.
Figure 13.
Real-space representation of active orbitals (HOMO and LUMO) for the ground state of GNP[m,15] with (a) , (b) , (c) , and (d) , at an isovalue of 0.02 e/Å3, calculated using spin-restricted TAO-LDA, where the orbital occupation numbers are shown in parentheses.
Figure 14.
Real-space representation of active orbitals (HOMO and LUMO) for the ground state of GNP[m,20] with (a) , (b) , (c) , and (d) , at an isovalue of 0.02 e/Å3, calculated using spin-restricted TAO-LDA, where the orbital occupation numbers are shown in parentheses.
Figure 14.
Real-space representation of active orbitals (HOMO and LUMO) for the ground state of GNP[m,20] with (a) , (b) , (c) , and (d) , at an isovalue of 0.02 e/Å3, calculated using spin-restricted TAO-LDA, where the orbital occupation numbers are shown in parentheses.