Department of Computational Science, Institute of Fundamental Technological Research Polish Academy of Sciences, Pawińskiego 5B, 02-106 Warsaw, Poland
Corresponding author email
Associate Editor: P. Leiderer Beilstein J. Nanotechnol.2023,14, 574–585.https://doi.org/10.3762/bjnano.14.48 Received 02 Mar 2023,
Accepted 25 Apr 2023,
Published 08 May 2023
The ability of various interatomic potentials to reproduce the properties of silicene, that is, 2D single-layer silicon, polymorphs was examined. Structural and mechanical properties of flat, low-buckled, trigonal dumbbell, honeycomb dumbbell, and large honeycomb dumbbell silicene phases, were obtained using density functional theory and molecular statics calculations with Tersoff, MEAM, Stillinger–Weber, EDIP, ReaxFF, COMB, and machine-learning-based interatomic potentials. A quantitative systematic comparison and a discussion of the results obtained are reported.
We are living in the “silicon age” because of the great importance of elemental silicon to the modern global economy, mainly concerning electronics. Silicon is one of the most extensively investigated materials, and the quality of its production is impressive. However, this applies to bulk silicon. The success of graphene has also sparked interest in other non-carbon 2D materials [1,2]. One of such materials is 2D silicon, called silicene [3,4]. Using first-principles methods with current computer resources enables us to model structures up to about a few hundred atoms. For larger systems, approximate methods are needed, for example, molecular dynamics/statics. For these methods, the quality of the used interatomic potentials (IAPs) is crucial. Because of the importance of silicon, as well as its complexity, dozens of potentials have been proposed for it. In the very well-known NIST Interatomic Potentials Repository, there are 27 potentials for silicon (the highest number of potentials provided for a chemical element), the oldest one from 1985 and the latest from 2020 [5,6].
At least five 2D silicon polymorphs have been reported in the literature, that is, flat (FS), low-buckled (LBS) [7], trigonal dumbbell (TDS), honeycomb dumbbell (HDS), and large honeycomb dumbbell (LHDS) silicene [8]. There are still doubts about their dynamic stability. For example, for a flat phase, the negative ZO phonon mode could be removed by the selection of an appropriate substrate [3,9]. The ability of potentials for 3D silicon to reproduce 2D silicon is poorly studied. There are several papers in which the quality of potentials for 3D silicon has been assessed [10-12], but not for silicene. The intention of this work is first to determine the structural and mechanical properties of 2D silicon using the first-principles method and then to test the ability of different interatomic potentials to reproduce these properties.
Methods
Analyzing the available literature concerning all phases of single-layer Si, it is not feasible to find all structural, mechanical, and phonon data obtained in one consistent way. The availability of experimental data is actually limited to the silicene grown on supports, a pristine free-standing single-layer sheet of silicene has not yet been discovered [4,13]. Therefore, we must use ab initio calculations. Unfortunately, also ab initio calculations, most often DFT, differ in the calculation methodology, that is, they use different functional bases and different pseudopotentials or exchange–correlation functionals. Also, parameters such as cohesive energy and elastic constants are poorly accessible. For this reason, structural and mechanical data, that is, lattice parameters, average cohesive energy, average bond length, average height, 2D elastic constants, as well as phonon data are determined here using a single consistent first-principles approach as described in the next section “Ab initio calculations”. These data were further considered as reference data and marked as “valueDFT”. Then, the same data were determined as described in section “Molecular calculations” using the analyzed molecular potentials from subsection “Interatomic potentials” and are marked as “valuepotential”. Having both data, we can simply define the mean absolute percentage error (MAPE):
which enables us to quantify the potentials under examination.
For 2D materials, directional 2D Young’s moduli,
2D Poisson’s ratios,
and the 2D shear modulus,
are often used instead of elastic constants Cij. Because of the symmetry of hexagonal lattices, these reduce to one 2D Young’s modulus E and one 2D Poisson’s ratio ν [14].
Ab initio calculations
The ab initio calculation methodology here is closely analogous to that used in [15]. Hence, its description is also very similar, that is, density functional theory (DFT) [16,17], ABINIT plane-wave approximation code [18,19], local density approximation (LDA) [20,21] as an exchange–correlation functional, and optimized norm-conserving Vanderbilt pseudopotential [22] (ONCVPP) are similar. Cut-off energy and electron configuration of Si were used in the DFT calculations according to the pseudopotential and Gaussian smearing scheme with tsmear (Ha) = 0.02. To generate k-points grids, kptrlen was set to 43.0. Since the 2D structures were analyzed in the z direction, a vacuum of 20 Å was applied. The initial data for the five structures analyzed were deduced from [7] and [8]. The structures were then carefully relaxed with full optimization of cell geometry and atomic coordinates [15].
The average cohesive energy Ec (eV/atom) was computed as the difference in the total energy of a given relaxed earlier structure and that of its individual atoms placed in a cubic box of sufficient size. The theoretical ground state, T = 0 K, and the elastic constants, Cij, of all previously optimized structures were computed using the metric tensor formulation of strain in the density functional perturbation theory (DFPT) [23]. The mechanical stability of the analyzed structures was verified by calculating the so-called Kelvin moduli [24,25]. To calculate the phonons, the DFPT implemented in ABINIT [18,19] was employed. The phonon dispersion curves along the path Γ[0,0,0]–M[1/2,0,0]–K[1/3,1/3,0]–Γ[0,0,0] [26] of the analyzed structures were then used to identify their dynamical stability [27], complementary to the mechanical stability.
Molecular calculations
To perform molecular calculations, the molecular statics (MS) method, T = 0 K [28-30], was used by means of the “large-scale atomic/molecular massively parallel simulator” (LAMMPS) [31] and analyzed by means of the “Open Visualization Tool” (OVITO) [32].
As for DFT calculations, the structures here were fully pre-relaxed with the conjugate gradient (CG) algorithm and, then, the elastic constants, Cij, were calculated for them using the stress–strain method with the maximum strain magnitude set to 10−6[30,31]. In the z direction, a vacuum was set to 20 Å.
To measure the performance of the analyzed interatomic potentials, a series of molecular dynamics (MD) simulations (200 atoms and 10000 timesteps, NVE ensemble) and LAMMPS’s built-in function timesteps/s were used. The results were then normalized relative to the longest run time.
Interatomic potentials
The parameterizations of the potentials listed below were obtained from the NIST Interatomic Potentials Repository and/or from LAMMPS code sources.
Tersoff1988 [33]: the original Tersoff potential for silicon (it is important to remember that this paper proposed a form of potential rather than a specific parametrization for silicon)
Tersoff2007 [34]: the Tersoff potential fitted to the elastic constants of diamond silicon
Tersoff2017 [35]: newer, better optimized the Tersoff potential for silicon
MEAM2007 [36]: the semi-empirical interatomic potential for silicon based on the modified embedded atom method (MEAM) formalism
MEAM2011 [37]: the spline-based modified embedded-atom method (MEAM) potential for Si fitted to silicon interstitials
SW1985 [38]: the Stillinger–Weber (SW) potential fitted to solid and liquid forms of Si
SW2014 [39]: the Stillinger–Weber (SW) potential fitted to phonon dispersion curves of a single-layer Si sheet
EDIP [40]: the environment-dependent interatomic potential (EDIP) fitted to various bulk phases and defect structures of Si
ReaxFF [41]: the reactive force-field (ReaxFF) fitted to a training set of DFT data that pertain to Si/Ge/H bonding environments
COMB [42]: the charge optimized many-body (COMB) potential fitted to a pure silicon and five polymorphs of silicon dioxide
SNAP [43]: the machine-learning-based (ML-IAP) linear variant of spectral neighbor analysis potential (SNAP) fitted to total energies and interatomic forces in ground-state Si, strained structures, and slab structures obtained from DFT calculations
qSNAP [43]: the machine-learning-based (ML-IAP) quadratic variant of spectral neighbor analysis potential (qSNAP) fitted to total energies and interatomic forces in ground-state Si, strained structures, and slab structures obtained from DFT calculations
SO(3) [44]: the machine-learning-based (ML-IAP) variant of the SO(3) smooth power spectrum potential (SO(3)) fitted to the ground-state of the crystalline silicon structure, strained structures, slabs, vacancy, and liquid configurations from DFT simulations
ACE [45]: the machine-learning-based (ML-IAP) variant of the atomic cluster expansion potential (ACE) fitted to a wide range of properties of 3D silicon determined from DFT calculations
Results and Discussion
Structural and mechanical properties
Basic cells for the five silicene polymorphs, that is, flat (FS) (hP2, P6/mmm, no.191), low-buckled (LBS) (hP2, , no.164), trigonal dumbbell (TDS) (hP7, , no.189), honeycomb dumbbell (HDS) (hP8, P6/mmm, no.191), and large honeycomb dumbbell (LHDS) (hP10, P6/mmm, no.191), are depicted in Figure 1. Additionally, the crystallographic data for them are stored in crystallographic information files (CIFs) in Supporting Information Files 1–5.
The results of first-principles calculations show that all silicene phases have hexagonal symmetry. The symmetry characteristics of a structure determine the symmetry of its physical properties (cf. Neumann’s Principle and Curie laws) [30,46]. For 2D linear hyperelastic materials, there are four classes of symmetry [25], and hexagonal symmetry implies isotropy of the stiffness tensor, that is, there are only two distinct elastic constants and they satisfy, in Voigt notation, such conditions that C11 = C22, C33 = (C11 − C12)/2.
Structural and mechanical characteristics that were determined from DFT computations, namely lattice parameters, average cohesive energy, average bond length, average height, 2D elastic constants, 2D Young’s modulus, Poisson’s ratio, and 2D Kelvin moduli, of the five silicene polymorphs analyzed are gathered in Table 1. Since we are analyzing free-standing silicene here, which has not yet been observed in experiments, we compare the results of the calculations with those of other authors. We find that the lattice constants, average bond length, average height and cohesive energy agree at the DFT level of accuracy with other calculations. Mechanical properties of silicene are available in the literature only for the LBS phase and are limited to 2D Young’s modulus and Poisson’s ratio only. These quantities are also in reasonable agreement with the present results. It is worth noting that all calculated 2D Kelvin moduli for all silicene phases are positive, which results in mechanical stability [25].
Table 1:
Structural and mechanical properties of flat (FS), low-buckled (LBS), trigonal dumbbell (TDS), honeycomb dumbbell (HDS), and large honeycomb dumbbell (LHDS) silicene phases from density functional theory (DFT) calculations: lattice parameters a and b (Å), average cohesive energy Ec (eV/atom), average bond length d (Å), average height h (Å), 2D elastic constants Cij (N/m), 2D Young’s modulus E (N/m), Poisson’s ratio ν, and 2D Kelvin moduli Ki (N/m).
Polymorph
FS
LBS
TDS
HDS
LHDS
Source
This work
Refs.
This work
Refs.
This work
Refs.
This work
Refs.
This work
Refs.
a
3.855
3.90a
3.828
3.87a, 3.83b
6.434
6.52c
6.297
6.38c
7.334
7.425c
b
3.855
3.90a
3.828
3.87a, 3.83b
6.434
6.52c
6.297
6.38c
7.334
7.425c
−Ec
4.562
4.764a
4.577
4.784a, 5.16b
4.679
4.679
4.769
dd
2.225
2.249
2.25b
2.331
2.399
2.357
h
0.0
0.0a
0.421a
0.45a, 0.44b
2.734
2.635
2.683
C11
84.8
69.2
100.5
141.6
104.5
C22
84.8
69.2
100.5
141.6
104.5
C12
40.6
22.1
52.3
96.4
52.7
C33
22.1
23.6
24.1
22.6
25.9
E
65.4
62.2
61.8a
73.3
76.0
77.9
ν
0.48
0.32
0.31a
0.52
0.68
0.50
KI
125.4
91.3
152.8
238.0
157.2
KII
44.3
47.1
48.3
45.2
51.8
KIII
44.3
47.1
48.3
45.2
51.8
aRef. [47], bRef. [7], cRef. [8], daverage bond lengths calculated using a radial pair distribution function with a cut-off radius of 3.0 Å and a number of histogram bins of 1000 [32].
The phonon spectra along the Γ-M-K-Γ path for the five silicene polymorphs is depicted in Figure 2. The analysis of the computed curves shows that the phases TDS, LBS, HDS, and LHDS are not only mechanically but also dynamically stable, that is, all phonon modes have positive frequencies anywhere. The FS phase is mechanically stable, but can be dynamically unstable, that is, the optical ZO phonon mode has a negative frequency. Other authors have also observed similar FS phase behavior [4,13]. However, since silicene is not a free-standing structure in nature, the selection of a proper substrate may dampen the out-of-plane vibration mode, and flat silicene may be produced [3]. Hence, it was decided to include also this phase in the molecular calculations.
Performance of interatomic potentials
Computations were carried out using molecular statics and the fourteen interatomic potentials for silicon, (Tersoff (×3), MEAM (×2), Stillinger–Weber (×2), EDIP, ReaxFF, COMB and machine-learning-based (ML-IAP (×4)), enumerated in section “Interatomic potentials”. Twelve structural and mechanical properties, that is, lattice parameters a and b, average cohesive energy Ec, average bond length d, average height h, 2D elastic constants Cij, and 2D Kelvin moduli Ki, are collected in Table 2 for flat silicene (FS), in Table 3 for low-buckled silicene (LBS), in Table 4 for trigonal dumbbell silicene (TDS), in Table 5 for honeycomb dumbbell silicene (HDS), and in Table 6 for large honeycomb dumbbell silicene (LHDS), respectively. The aforementioned results, for each of the five silicene phases, were then compared with those from DFT calculations using the mean absolute percentage error (MAPE) defined in Equation 1. Let us briefly analyze the results for each phase. For the FS phase, the most accurate is the MEAM2011 potential as it has the lowest MAPE , see Table 2. For the LBS phase, it is the Tersoff2107 potential, see Table 3. For the TDS phase, it is the ReaxFF potential, see Table 4. For the HDS phase, it is the ReaxFF potential, see Table 5, and finally, for the LHDS phase, it is again the ReaxFF potential, see Table 6. Now let us take a summary look. Seven of the analyzed fourteen potentials, namely Tersoff2007, Tersoff2017, SW1985, SW2014, ReaxFF, SNAP, and ACE, are able to correctly reproduce the structural properties of the five polymorphs of silicene, see Table 3, Table 5, and Table 6. Two potentials, ReaxFF and MEAM2011, give the best quantitative performance measured by the total mean absolute percentage error (MAPE), see Table 6. Regarding the cost of calculations in terms of relative performance measured as normalized timesteps per second in molecular dynamics (MD) simulations, the EDIP and Stillinger–Weber potentials are the fastest, about five times faster than the MEAM and Tersoff potentials, about 100 times faster than ReaxFF and COMB, and up to 2000 times faster than the ML-IAP(ACE) potential, see Table 6. It is also worth noting, that the machine-learning-based (ML-IAP) interatomic potentials, according to the methodology used, are not superior to classical potentials in terms of performance (MAPE) and are instead even three orders of magnitude more computationally expensive, see Table 6.
Table 2:
Structural and mechanical properties of flat silicene (FS) from molecular calculations: lattice parameters a and b (Å), average cohesive energy Ec (eV/atom), average bond length d (Å), average height h (Å), 2D elastic constants Cij (N/m), 2D Kelvin moduli Ki (N/m), and mean absolute percentage error (MAPE) (%).
Method
DFT
Tersoff1988
Tersoff2007
Tersoff2017
MEAM2007
MEAM2011
SW1985
SW2014
a
3.855
4.008
4.019
4.042
4.457
3.960
4.104
3.886
b
3.855
4.008
4.019
4.042
4.457
3.960
4.104
3.886
−Ec
4.562
3.926
3.828
3.687
3.288
3.793
3.145
2.564
d
2.225
2.315
2.321
2.333
2.573
2.288
2.369
2.243
h
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
C11
84.8
54.6
55.2
47.7
57.3
84.0
58.8
57.3
C22
84.8
54.6
55.2
47.7
57.3
84.0
58.8
57.3
C12
40.6
49.5
47.4
52.8
29.7
40.8
34.4
33.0
C33
22.1
2.6
3.9
−2.6
13.8
21.6
12.2
12.2
KI
125.4
104.0
102.6
100.5
87.0
124.8
93.2
90.3
KII
44.3
5.1
7.8
−5.1
27.6
43.3
24.4
24.4
KIII
44.3
5.1
7.8
−5.1
27.6
43.3
24.4
24.4
MAPEFS
36.515
34.619
45.987
28.174
3.147
26.093
26.595
Method
EDIP
ReaxFF
COMB
ML-IAP SNAP
ML-IAP qSNAP
ML-IAP SO(3)
ML-IAP ACE
a
4.018
3.950
3.990
4.121
4.019
4.051
3.850
b
4.018
3.950
3.990
4.121
4.019
4.051
3.850
−Ec
4.010
3.408
3.911
4.575
4.499
4.407
0.962
d
2.321
2.282
2.306
2.381
2.321
2.340
2.222
h
0.0
0.0
0.0
0.0
0.0
0.0
0.0
C11
87.4
74.7
79.7
41.5
24.7
41.0
88.9
C22
87.4
74.7
79.7
41.5
24.7
41.0
88.9
C12
9.2
36.8
23.9
16.2
17.6
23.9
44.8
C33
39.1
19.0
27.9
12.6
3.5
8.6
22.0
KI
96.6
111.5
103.6
57.6
42.3
64.9
133.7
KII
78.2
37.9
55.8
25.3
7.0
17.1
44.1
KIII
78.2
37.9
55.8
25.3
7.0
17.1
44.1
MAPEFS
32.827
10.904
15.805
33.284
48.283
35.946
9.757
Table 3:
Structural and mechanical properties of low-buckled silicene (LBS) from molecular calculations: lattice parameters a and b (Å), average cohesive energy Ec (eV/atom), average bond length d (Å), average height h (Å), 2D elastic constants Cij (N/m), 2D Kelvin moduli Ki (N/m), and mean absolute percentage error (MAPE) (%).
Method
DFT
Tersoff1988
Tersoff2007
Tersoff2017
MEAM2007
MEAM2011
SW1985
SW2014
a
3.828
3.309
3.820
3.870
4.150
3.837
3.840
3.812
b
3.828
3.309
3.820
3.870
4.150
3.837
3.840
3.812
−Ec
4.577
3.936
3.936
3.755
3.404
3.851
3.252
2.572
d
2.249
2.315
2.312
2.315
2.534
2.297
2.351
2.243
h
0.421
1.304
0.690
0.327
0.820
0.608
0.784
0.427
C11
69.2
0.006
59.6
50.1
38.4
47.1
36.4
30.8
C22
69.2
0.006
59.6
50.1
38.4
47.1
36.4
30.8
C12
22.1
0.002
4.1
5.5
4.4
10.6
6.1
5.4
C33
23.6
0.002
27.7
22.3
17.0
18.3
15.1
12.7
KI
91.3
0.008
63.7
55.7
42.7
57.7
42.6
36.2
KII
47.1
0.004
55.5
44.6
34.0
36.5
30.3
25.5
KIII
47.1
0.004
55.5
44.6
34.0
36.5
30.3
25.5
MAPELBS
79.467
22.781
19.235
37.991
23.569
37.306
35.911
Method
EDIP
ReaxFF
COMB
ML-IAP SNAP
ML-IAP qSNAP
ML-IAP SO(3)
ML-IAP ACE
a
4.018
3.843
3.990
3.916
3.808
3.743
3.702
b
4.018
3.843
3.990
3.916
3.808
3.743
3.702
−Ec
4.010
3.454
3.911
4.648
4.624
4.606
0.926
d
2.321
2.300
2.306
2.390
2.366
2.347
2.242
h
0.0a
0.610
0.0a
0.774
0.859
0.913
0.675
C11
87.4
49.0
79.7
24.8
28.8
31.7
55.0
C22
87.4
49.0
79.7
24.8
28.8
31.7
55.0
C12
9.2
14.1
23.9
5.6
6.3
9.0
2.7
C33
39.1
17.5
27.9
9.6
11.2
11.4
26.1
KI
96.6
63.1
103.6
30.4
35.1
40.6
57.7
KII
78.2
34.9
55.8
19.2
22.4
22.7
52.3
KIII
78.2
34.9
55.8
19.2
22.4
22.7
52.3
MAPELBS
36.630
22.977
19.433
45.306
43.176
42.060
28.792
aInput LBS converges to FS.
Table 4:
Structural and mechanical properties of trigonal dumbbell silicene (TDS) from molecular calculations: lattice parameters a and b (Å), average cohesive energy Ec (eV/atom), average bond length d (Å), average height h (Å), 2D elastic constants Cij (N/m), 2D Kelvin moduli Ki (N/m), and mean absolute percentage error (MAPE) (%).
Method
DFT
Tersoff1988
Tersoff2007
Tersoff2017
MEAM2007
MEAM2011
SW1985
SW2014
a
6.434
6.480
6.471
6.475
7.140
6.511
6.600
6.291
b
6.434
6.480
6.471
6.475
7.140
6.511
6.600
6.291
−Ec
4.679
4.248
3.865
3.890
3.427
3.865
3.322
2.591
d
2.331
2.362
2.371
2.362
2.590
2.376
2.431
2.315
h
2.734
2.870
3.110
3.056
3.228
2.856
3.260
3.100
C11
100.5
79.4
67.9
51.4
69.9
80.5
78.7
68.4
C22
100.5
79.4
67.9
51.4
69.9
80.5
78.7
68.4
C12
52.3
63.2
39.4
39.0
34.9
31.0
39.6
35.1
C33
24.1
8.1
14.3
6.2
17.5
24.8
19.6
16.7
KI
152.8
142.7
107.3
90.5
104.8
111.5
118.2
103.5
KII
48.3
16.2
28.5
12.4
35.0
49.5
39.1
33.3
KIII
48.3
16.2
28.5
12.4
35.0
49.5
39.1
33.3
MAPETDS
23.790
22.993
34.815
23.821
11.809
17.077
23.723
Method
EDIP
ReaxFF
COMB
ML-IAP SNAP
ML-IAP qSNAP
ML-IAP SO(3)
ML-IAP ACE
a
6.759
6.380
6.574
6.777
6.797
6.518
6.448
b
6.759
6.380
6.574
6.777
6.797
6.518
6.448
−Ec
4.075
3.551
3.723
4.726
4.693
4.607
0.4606
d
2.416
2.344
2.387
2.398
2.404
2.505
2.429
h
2.628
3.115
2.994
2.518
2.540
3.058
2.649
C11
60.3
94.6
70.8
44.9
36.7
35.3
90.5
C22
60.3
94.6
70.8
44.9
36.7
35.3
90.5
C12
30.2
39.7
25.6
16.1
16.7
10.2
33.0
C33
15.1
27.4
22.6
14.4
10.0
12.5
28.8
KI
90.5
134.3
96.4
61.1
53.4
45.5
123.5
KII
30.1
54.9
45.2
28.8
20.1
25.0
57.6
KIII
30.1
54.9
45.2
28.8
20.1
25.0
57.6
MAPETDS
25.537
10.755
16.881
31.929
38.100
37.357
19.317
Table 5:
Structural and mechanical properties of honeycomb dumbbell silicene (HDS) from molecular calculations: lattice parameters a and b (Å), average cohesive energy Ec (eV/atom), average bond length d (Å), average height h (Å), 2D elastic constants Cij (N/m), 2D Kelvin moduli Ki (N/m), and mean absolute percentage error (MAPE) (%).
Method
DFT
Tersoff1988
Tersoff2007
Tersoff2017
MEAM2007
MEAM2011
SW1985
SW2014
a
6.297
6.074a
6.133
6.114
5.608a
6.364a
6.272
6.063
b
6.297
5.864a
6.133
6.114
6.127a
6.279a
6.272
6.063
−Ec
4.679
4.334
3.738
3.816
3.663
4.008
3.243
2.467
d
2.399
2.398
2.425
2.408
2.641
2.405
2.495
2.394
h
2.635
2.596
3.050
3.006
3.500
2.990
3.189
3.010
C11
141.6
89.3
59.4
64.0
56.2
78.2
57.4
C22
141.6
46.3
59.4
64.0
57.1
82.4
57.4
C12
96.4
20.4
24.6
31.3
17.3
7.1
25.3
14.9
C33
22.6
24.8
17.4
16.3
16.7
26.2
16.0
10.4
KI
238.0
102.1
84.0
95.3
76.6
87.8
82.7
50.6
KII
45.2
57.7
34.8
32.7
44.5
72.9
32.0
20.7
KIII
45.2
25.5
34.8
32.7
−1.2b
52.3
32.0
20.7
MAPEHDS
28.361
30.540
29.931
39.880
30.392
33.503
45.400
Method
EDIP
ReaxFF
COMB
ML-IAP SNAP
ML-IAP qSNAP
ML-IAP SO(3)
ML-IAP ACE
a
6.349a
6.048
6.344
6.644
6.510a
5.030a
6.250
b
6.389a
6.048
6.344
6.644
6.344a
5.818a
6.250
−Ec
4.105
3.366
3.481
4.766
4.699
4.873
0.391
d
2.489
2.396
2.511
2.436
2.457
2.612
2.371
h
2.365
2.952
2.989
2.404
2.438
2.162
2.559
C11
78.0
75.4
14.3
41.9
30.8
76.5
69.6
C22
45.5
75.4
14.3
41.9
16.0
73.0
69.6
C12
46.3
31.4
−37.4
16.2
13.8
−10.2
10.6
C33
9.6
22.0
25.8
12.9
11.7
2.7
29.5
KI
119.7
106.8
51.7
58.1
41.2
84.9
80.1
KII
13.6
43.9
−23.1b
25.7
24.3
64.7
59.0
KIII
9.2
43.9
51.7
25.7
23.4
5.5
59.0
MAPEHDS
37.500
22.740
51.780
37.684
41.062
45.571
37.134
aPotential does not reproduce the correct symmetry of the structure (a ≠ b), bnegative Kelvin moduli Ki indicating a lack of mechanical stability.
Table 6:
Structural and mechanical properties of large honeycomb dumbbell silicene (LHDS) from molecular calculations: lattice parameters a and b (Å), average cohesive energy Ec (eV/atom), average bond length d (Å), average height h (Å), 2D elastic constants Cij (N/m), 2D Kelvin moduli Ki (N/m), mean absolute percentage error (MAPE) (%), and relative performance measured as normalized timesteps/second in molecular dynamics (MD) simulation.
Method
DFT
Tersoff1988
Tersoff2007
Tersoff2017
MEAM2007
MEAM2011
SW1985
SW2014
a
7.334
7.000a
7.249
7.236
7.900a
7.363
7.403
7.062
b
7.334
6.978a
7.249
7.236
7.560a
7.363
7.403
7.062
−Ec
4.769
4.468
3.897
4.004
3.505
3.911
3.399
2.602
d
2.357
2.381
2.387
2.369
2.627
2.407
2.456
2.345
h
2.683
2.692
3.109
3.050
3.050
2.857
3.250
3.100
C11
104.5
2.3
78.5
68.6
45.6
88.0
84.5
73.0
C22
104.5
7.3
78.5
68.6
56.2
88.0
84.5
73.0
C12
52.7
−13.1
42.3
25.5
22.0
29.9
46.1
38.9
C33
25.9
9.0
18.1
21.6
14.0
29.0
19.2
17.1
KI
157.2
18.5
120.8
94.1
73.6
117.9
130.6
111.9
KII
51.8
17.8
36.2
43.1
28.8
58.1
38.4
34.1
KIII
51.8
−8.5b
36.2
43.1
27.4
58.1
38.4
34.1
MAPELHDS
55.693
18.373
20.287
34.499
13.636
16.741
23.855
223.826
129.306
150.256
164.364
82.553
130.721
155.483
timesteps/s
387.2
382.7
355.2
505.7
416.9
904.9
1753.8
Method
EDIP
ReaxFF
COMB
ML-IAP SNAP
ML-IAP qSNAP
ML-IAP SO(3)
ML-IAP ACE
a
7.705
7.167
7.422
7.648
7.741
7.427
7.393
b
7.705
7.167
7.422
7.648
7.741
7.427
7.393
−Ec
4.113
3.623
3.646
4.804
4.794
4.698
0.370
d
2.438
2.370
2.454
2.436
2.403
2.385
2.515
h
2.637
3.112
2.994
2.538
2.562
2.700
2.712
C11
53.7
99.0
53.1
44.6
43.8
25.9
59.8
C22
53.7
99.0
53.1
44.6
43.8
25.9
59.8
C12
36.0
40.4
31.7
16.1
19.1
0.4
15.2
C33
8.8
29.3
10.7
14.2
12.3
12.7
22.3
KI
89.7
139.4
84.8
60.6
62.8
26.4
75.0
KII
17.6
58.6
21.5
28.5
24.7
25.5
44.7
KIII
17.6
58.6
21.5
28.5
24.7
25.5
44.7
MAPELHDS
33.228
10.809
33.466
33.219
34.603
40.928
29.310
165.722
78.185
137.365
181.422
205.224
201.861
124.310
timesteps/s
2032.1
23.6
26.4
7.9
4.7
4.2
1.0
aDoes not reproduce the correct symmetry of the structure (a ≠ b), bnegative Kelvin moduli Ki indicating a lack of mechanical stability.
Conclusion
A systematic quantitative comparative study of various silicon interatomic potentials for reproducing the properties of five silicene (2D silicon) polymorphs was shown. In order to compare the fourteen potentials listed in section “Interatomic potentials”, the structural and mechanical properties of flat (FS), low-buckled (LBS), trigonal dumbbell (TDS), honeycomb dumbbell (HDS), and large honeycomb dumbbell (LHDS) silicene (Figure 1) obtained from density functional theory (DFT) and molecular statics (MS) computations were used. Computational cost and performance of the analyzed potentials were compared.
Considering the performance and the cost of calculations, the classical potentials of Tersoff, SW, and MEAM types seem to be the best choice here. Although data for silicene polytypes were not used in the optimization of these potentials, they were able to reproduce their properties well. This is a consequence of the fact that they are based on physics, have a natural extrapolation ability, and do not just interpolate data.
I hope that the findings presented here will help other researchers in selecting the suitable potentials for their purposes and will be a hint to parameterize new potentials for silicene.
Supporting Information
Crystallographic Information Files (CIF) for polymorphs of silicene (created by qAgate (Opensource software to post-process ABINIT) and then converted to P1 setting by Bilbao Crystallographic Server [48,49]).
Supporting Information File 1:
Flat silicene (FS).
Additional assistance was granted through the computing cluster GRAFEN at Biocentrum Ochota, the Interdisciplinary Centre for Mathematical and Computational Modelling of Warsaw University (ICM UW) and Poznań Supercomputing and Networking Center (PSNC).
Funding
This work was supported by the National Science Centre (NCN – Poland) Research Project: No. 2021/43/B/ST8/03207.
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