# Long-range corrected DFT calculations of charge-transfer integrals in model metal-free phthalocyanine complexes

## Abstract

An assessment of several widely used exchange--correlation potentials in computing charge-transfer integrals is performed. In particular, we employ the recently proposed Coulomb-attenuated model which was proven by other authors to improve upon conventional functionals in the case of charge-transfer excitations. For further validation, two distinct approaches to compute the property in question are compared for a phthalocyanine dimer.

### Keywords

Charge-transfer integral Density functional theory Long-range corrected functionals Organic electronics Phthalocyanine## Introduction

*J*) which describes the transport of a charge between adjacent molecular sites. An inherent issue of practical computations of charge–transfer integrals represents the choice of an approach to solve the Schrödinger equation. Currently, the DFT framework is commonly used to model charge transport in organic materials [3, 8, 9, 10, 11, 12, 13, 14]. Certain exchange–correlation potentials are recognized to predict accurately geometries of molecules and shapes of molecular orbitals. However, it is well known, that wrong asymptotic behavior of conventional functionals create a real problem in calculations of some properties, especially for molecular complexes [15, 16]. A recent systematic study of Peach and co–workers may serve as an illustrative example [17]. The authors showed that conventional exchange–correlation functionals have difficulties with reliable description of excitation energies to charge–transfer states in molecules and molecular complexes. The charge–transfer integral (

*J*) involves orbitals localized on the two adjacent sites. For this reason, its evaluation might also present a challenge for the conventional exchange–correlation potentials commonly used nowadays. The primary goal of this study is to shed some light on this issue by employing recently proposed long–range corrected density functional theory (hereafter denoted as LRC–DFT) to compute charge–transfer integrals. The LRC–DFT is still being extensively tested primarily with an eye toward electric dipole (hyper)polarizabilities, linear and nonlinear optical spectra [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]. Here, we use two LRC functionals, namely LC-BLYP [30] and CAM–B3LYP [31] together with their conventional counterparts. The LRC functionals employ the Ewald split of \( r_{{12}}^{{^{{ - {1}}}}} \) operator which, in the case of the CAM-B3LYP functional, takes the following form [31]:

*μ*= 0.33 for CAM–B3LYP and

*μ*= 0.47 for LC–BLYP [32].

The absolute values of effective charge transfer integrals (|*J*_{eff}|, given in eV) computed with the aid of Eqs. (3)–(6) and the charge transfer integrals calculated using energy splitting in dimer approach (|∆/2|). The cc-pVDZ basis set was employed in all calculations. *R* is the intermolecular distance

As a model system to evaluate the performance of conventional exchange–correlation potentials in computing charge–transfer integrals we have chosen metal–free phthalocyanine dimer. Phthalocyanines are often considered as conductive materials with potential applications in organic electronics [33, 34, 35, 36]. In crystalline phase phthalocyanine molecules usually form regular columns and liquid crystals composed of phthalocyanines are promising materials for organic electronics [37]. The liquid crystals in question are usually built from flat aromatic phthalocyanine center and aliphatic side groups. Likewise, aromatic core of molecules in liquid crystal state form regular columns with molecules in stacked conformations and the fastest charge transport is observed inside a column with much smaller probability of charge transport between columns. The charge–transfer integral between monomers in dimer can be used to describe charge transport inside of column composed of phthalocyanine molecules and as a first approximation of charge–transfer in phthalocyanine based liquid crystals. In this work only charge–transfer integrals between highest occupied molecular orbitals (HOMOs) of adjacent monomers are considered. This represents the charge–transfer integral related to the transport of positive charge carrier (hole transport).

## Computational details

*h*

_{KS}is the Kohn–Sham Hamiltonian, Ψ

*s*, Ψ

*s*′ denote wave functions of the charge carrier localized on the sites

*s*and

*s*′, respectively. In many organic systems there is non–zero spatial overlap between orbitals of the molecular sites. To account for this effect in calculations of charge–transfer integrals, the effective charge–transfer integral (

*J*

_{eff}) may be introduced [4]:

*S*

_{s,s′}denotes overlap integral of orbitals

*s*and

*s*′; ε

_{s}and ε

_{s′}stand for energies of the sites

*s*and

*s*′, respectively, and hereafter will be referred to as site energies.

*h*

_{KS}) in the basis of molecular orbitals of the monomers [3, 38]:

*E*), the eigenvectors in the basis of atomic orbitals (AO) for a dimer (

*C*

_{AO}), the spatial overlap integrals in AO representation for a dimer (

*S*

_{AO}) and eigenvectors for monomers in AO representation \( (C_{{AO}}^{\prime }{\hbox{and}} C_{{AO}}^{{\prime\!\,\prime }}) \).The eigenvector matrix for a dimer and spatial overlap matrix was transformed from AO representation to molecular orbital representation of monomers as follows:

*A*denotes transformation matrix which is diagonal block matrix with monomer eigenvector matrices \( (C_{{AO}}^{\prime }{\hbox{and}} C_{{AO}}^{{\prime\!\,\prime }}) \) on the diagonal and

*A*

^{T}is transposed transformation matrix. The off–diagonal elements of

*h*

_{KS}matrix in monomer orbital basis represent charge–transfer integrals.

*s*th molecular orbital of the Hamiltonian for dimer is given by:

_{s}is the

*s*–th site energy integral,

*J*

_{s}and

*S*

_{s}are the charge–transfer and the overlap integral between orbitals denoted by index

*s*of two molecules forming dimer, respectively. It is assumed that the mixing between

*s*–th orbital and the other molecular orbitals is not significant. The splitting in energy of

*s*–th molecular orbital of monomer in a dimer can be written as:

*E*

_{s,A}and

*E*

_{s,B}denote eigenvalues for

*s*–th molecular orbital of molecules A and B, respectively.

*J*

_{s}, α

_{s},

*S*

_{s}stand for the charge–transfer integral, site energy and overlap integral of

*s*–th molecular orbital of molecules A and B, respectively. Since we consider a dimer composed of two identical monomers, it is further assumed that α

_{s}is the same for monomers A and B. For a system composed of two nonequivalent molecules, expressions for the eigenvalue

*E*

_{s}and the charge transfer integral

*J*

_{s}take more complicated form [41]. Usually, it is also assumed that spatial overlap integral is equal zero (

*S*

_{s}= 0), which is reasonable assumption considering charge transport between two organic molecules. In organic materials spatial overlap between orbitals of neighboring molecules are usually <<1. Thus, Eq. (8) can be rewritten as:

*J*can be calculated according to this relation.

Calculations were performed with the aid of several exchange–correlation potentials using different basis sets, including Dunning’s correlation consistent cc-pVDZ basis set [42] as well as recently proposed Jensen’s basis set [43]. The results of calculations presented in this work were carried out using the GAUSSIAN 09 program [32].

## Results and discussion

The values of the spatial overlap for different geometrical parameters calculated with the aid of different exchange–correlation potentials as well as using the Hartree–Fock method are presented in Fig. 2b. As seen in the figure, the values of the spatial overlap calculated with use of different DFT functionals are comparable for all considered geometrical parameters. The values of spatial overlap integral calculated using the HF wavefunction seem to be overestimated and differ substantially from the values determined within the DFT framework.

*J*) and effective charge–transfer integral (

*J*

_{eff}) is concerned. The commonly used conventional exchange–correlation potentials such as BLYP or B3LYP predict much smaller values of charge–transfer integrals than the HF method. The PW91 functional, suggested as the best choice for computations of charge–transfer integrals in π–conjugated systems in stacked configurations [3, 41], gives comparable results to those determined with the aid of the BLYP and the B3LYP potentials. The values of charge–transfer integrals predicted by long–range corrected functionals, namely CAM–B3LYP and LC-BLYP, lie between HF and conventional DFT results (see Fig. 3). It was shown by Peach and co-workers that long–range corrected functionals improve substantially upon their traditional counterparts as far as excitation energies to charge–transfer states are concerned [17]. It is a particularly notable observation for the Coulomb–attenuated model (CAM-B3LYP). Since both quantities in principle might be similar in nature, the LRC potentials should give better results also in the case of charge–transfer integrals. For this reason, with a bit of scepticism due to the lack of more solid quantitative basis, we use the CAM-B3LYP potential as a reference. We conclude that conventional DFT functionals underestimate the values of charge–transfer integrals in comparison with their LRC counterparts.

*i*and

*j*is defined as:

*h*,

*k*

_{B}and

*T*are Planck constant, Boltzmann constant and temperature, respectively. ε

_{i}and ε

_{j}denote energies of the charge carrier localized on the sites

*i*and

*j*and λ stands for reorganization energy [4, 13, 46]. In order to estimate the charge carrier mobility in the system without structural disorder (assuming that each hopping rate is the same) one can use the relation [47, 48]:

*e*is elementary charge, and

*a*denotes distance between molecular sites. For the studied phthalo-cyanine dimers, the internal reorganization energy calculated at the B3LYP/cc–pVDZ level of theory is 0.043 eV, which is similar to the results presented in literature [13, 49, 50]. For the intermolecular distance 3.5 Å, the rotation angle 0 and lateral slide 1.5 Å (this is the structure similar to the crystal structure of the phthalocyanine) the charge–transfer integrals calculated with B3LYP and CAM–B3LYP functional are −0.16 eV and −0.18 eV respectively. The charge carrier mobility values calculated from Eq. (11) for this two charge–transfer integrals are 3.9 cm

^{2}/Vs and 4.9 cm

^{2}/Vs. Thus, one can easily see, that DFT functional has a significant influence on the mobility value. A close look at Fig. 2 leads to the conclusion that for certain areas of conformational space the differences might be even higher.

A comparison of the effective charge-transfer integrals calculated using Eq. (3) and the charge-transfer integrals determined from the energy splitting (Eq. (9)) is shown in Table 1. The data show that the differences in the values of charge–transfer integrals calculated based on the two approaches are insignificant and do not exceed a few thousandths of eV. At first glance, it appears that it is sufficient to employ less accurate method, based on the energy splitting in dimer with assumption of zero spatial overlap, to compute *J* between molecules in π interacting system. However, as it has already been mentioned, it is important to include spatial overlap in calculations of charge–transfer integrals from the definition. Otherwise, the values of *J* might strongly depend on the size of the basis set used in calculations. The other drawback of the method based on energy splitting in dimer is the lack of information about the sign of charge–transfer integral. However, if the knowledge of the sign is important, it can be subsequently determined from the bonding–antibonding character of the interaction between the corresponding orbitals [51].

## Conclusions

The primary aim of the present study was to evaluate the performance of commonly employed conventional exchange–correlation potentials that are used to compute charge–transfer integrals. In doing so, we apply the recently proposed Coulomb–attenuated model as a reference as this approach is proven to be very successful in predicting excitation energies to charge–transfer states. It is shown that for certain areas of conformational space in phthalocyanine dimer the differences in values of charge-transfer integrals between the conventional schemes and the CAM-B3LYP functional in values of charge–transfer integrals might be quite significant. The same is revealed for triphenylene dimer [52]. As a result, the values of charge carrier mobilities estimated using Marcus formula might differ by 20% and more. Likewise, theoretical predictions of peaks intensity in electro-absorption spectrum of molecular crystals and molecular aggregates [53, 54] might be determined to a large extent by the accuracy of charge-transfer integrals (Kulig W, Petelenz P, (2010). Private communication). We have also confirmed the findings reported by other authors [41] that the size of the basis set used in calculations of charge–transfer integrals plays only a minor role provided the spatial overlap is included in the theoretical model.

## Notes

### Acknowledgments

This work was supported by computational grants from Wroclaw Center for Networking and Supercom- puting (WCSS) and ACK Cyfronet. Work in the USA was supported by the HRD-0833178 grant. One of the authors (RZ) would like to acknowledge support from a grant from Iceland, Liechtenstein and Norway through the EEA Financial Mechanism - Scholarship and Training Fund. Financial support from Wroclaw University of Technology and the Czech Science Foundation (Project No. P205/10/2280) and the European Commission through the Human Potential Programme (Marie-Curie RTN BIMORE, Project No. MRTN-CT-2006-035859) is also acknowledged.

### Open Access

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

### References

- 1.Brédas JL, Calbert JP, da Silva Filho DA, Cornil J (2002) PNAS 99(9):5804CrossRefGoogle Scholar
- 2.Wang L, Nan G, Yang X, Peng Q, Li Q, Shuai Z (2010) Chem Soc Rev 39:423CrossRefGoogle Scholar
- 3.Senthilkumar K, Grozema FC, Bickelhaupt FM, Siebbeles LDA (2003) J Chem Phys 119:9809CrossRefGoogle Scholar
- 4.Newton MD (1991) Chem Rev 91:767CrossRefGoogle Scholar
- 5.Cheung DL, Troisi A (2008) Phys Chem Chem Phys 10:5941CrossRefGoogle Scholar
- 6.Bassler H (1993) Phys Stat Sol (b) 175:15CrossRefGoogle Scholar
- 7.Toman P, Nespurek S, Bartkowiak W (2009) Mater Sci Poland 27:797Google Scholar
- 8.Hreha RD, George CP, Haldi A, Domercq B, Malagoli M, Barlow S, Brédas J-L, Kippelen B, Marder SR (2003) Adv Funct Mater 13:967CrossRefGoogle Scholar
- 9.Yang B, Kim S-K, Xu H, Park Y-I, Zhang H, Gu C, Shen F, Wang C, Liu D, Liu X, Hanif M, Tang S, Li W, Li F, Shen J, Park J-W, Ma Y (2008) Chem Phys Chem 9:2601Google Scholar
- 10.Gao H, Zhang H, Mo R, Sun S, Su Z-M, Wang Y (2009) Synth Met 159:1767CrossRefGoogle Scholar
- 11.Zbiri M, Johnson MR, Kearley GJ, Mulder FM (2010) Theor Chem Acc 125:445CrossRefGoogle Scholar
- 12.Delgado MCR, Kim EG, da DA, Filho S, Brédas JL (2010) J Am Chem Soc 132:3375CrossRefGoogle Scholar
- 13.Lee C, Sohlberg K (2010) Chem Phys 367:7CrossRefGoogle Scholar
- 14.Chen S, Ma J (2009) J Comp Chem 30:1959CrossRefGoogle Scholar
- 15.Baerends EJ, Gritsenko OV (1997) J Phys Chem A 101:5383CrossRefGoogle Scholar
- 16.Tozer DJ, Amos RD, Handy NC, Roos BO, Serrano-Andres L (1999) Mol Phys 97:859CrossRefGoogle Scholar
- 17.Peach MJG, Benfield P, Helgaker T, Tozer DJ (2008) J Chem Phys 128:044118CrossRefGoogle Scholar
- 18.Kamiya M, Sekino H, Tsuneda T, Hirao K (2005) J Chem Phys 122:234111CrossRefGoogle Scholar
- 19.Kirtman B, Bonness S, Ramirez-Solis A, Champagne B, Matsumoto H, Sekino H (2008) J Chem Phys 128:114108CrossRefGoogle Scholar
- 20.Jacquemin D, Perpéte EA, Scalmani G, Frisch MJ, Kobayashi R, Adamo C (2007) J Chem Phys 126:144105CrossRefGoogle Scholar
- 21.Loboda O, Zaleśny R, Avramopoulos A, Luis JM, Kirtman B, Tagmatarchis N, Reis H, Papadopoulos MG (2009) J Phys Chem A 113:1159CrossRefGoogle Scholar
- 22.Jacquemin D, Perpète EA, Medved M, Scalmani G, Frisch MJ, Kobayashi R, Adamo C (2007) J Chem Phys 126:191108CrossRefGoogle Scholar
- 23.Hammond JR, Kowalski K (2009) J Chem Phys 130:194108CrossRefGoogle Scholar
- 24.Limacher PA, Mikkelsen KV, Lüthi HP (2009) J Chem Phys 130:194114CrossRefGoogle Scholar
- 25.Zaleśny R, Wójcik G, Mossakowska I, Bartkowiak W, Avramopoulos A, Papadopoulos MG (2009) J Mol Struct THEOCHEM 901:46CrossRefGoogle Scholar
- 26.Casida ME, Salahub DR (2000) J Chem Phys 113:8918CrossRefGoogle Scholar
- 27.Cai ZL, Crossley MJ, Reimers JR, Kobayashi R, Amos RD (2006) J Phys Chem B 110:15624CrossRefGoogle Scholar
- 28.Silva D, Krawczyk P, Bartkowiak W, Mendonca CR (2009) J Chem Phys 131:244516CrossRefGoogle Scholar
- 29.Rostov IV, Amos RD, Kobayashi R, Scalmani G, Frisch MJ (2010) J Phys Chem B 114:5547CrossRefGoogle Scholar
- 30.Iikura H, Tsuneda T, Yanai T, Hirao K (2001) J Chem Phys 115:3540CrossRefGoogle Scholar
- 31.Yanai T, Tew DP, Handy NC (2004) Chem Phys Lett 393:51CrossRefGoogle Scholar
- 32.Frisch MJ et al (2009) Gaussian 09 Revision A.1. Gaussian Inc. Wallingford CTGoogle Scholar
- 33.Facchetti A (2007) Mater Today 10(3):28CrossRefGoogle Scholar
- 34.Newman CR, Frisbie CD, da Silva Filho DA, Brédas JL, Ewbank PC, Mann KR (2004) Chem Mater 16:4436CrossRefGoogle Scholar
- 35.Toman P, Nespurek S, Yakushi K (2002) J Porphyr Phthalocyanines 6:556CrossRefGoogle Scholar
- 36.Shirota Y, Kageyama H (2007) Chem Rev 107:953CrossRefGoogle Scholar
- 37.Sergeyev S, Pisula W, Geerts YH (2007) Chem Soc Rev 36:1902CrossRefGoogle Scholar
- 38.Mikolajczyk MM, Toman P, Bartkowiak W (2010) Chem Phys Lett 485:253CrossRefGoogle Scholar
- 39.Cornil J, Beljonne D, Calbert JP, Brédas JL (2001) Adv Mater 13(14):1053CrossRefGoogle Scholar
- 40.Huang J, Kertesz M (2004) Chem Phys Lett 390:110CrossRefGoogle Scholar
- 41.Huang J, Kertesz M (2005) J Chem Phys 122:234707CrossRefGoogle Scholar
- 42.Dunning TH (1989) J Chem Phys 90:1007CrossRefGoogle Scholar
- 43.Jensen F (2001) J Chem Phys 115:9113CrossRefGoogle Scholar
- 44.Marcus RA (1993) Rev Mod Phys 65:599CrossRefGoogle Scholar
- 45.Barbara PF, Meyer TJ, Ratner MA (1996) J Phys Chem 100:13148CrossRefGoogle Scholar
- 46.Hutchison GR, Ratner MA, Marks TJ (2005) J Am Chem Soc 127:2339CrossRefGoogle Scholar
- 47.Grozema FC, Siebbeles LDA (2008) Int Rev Phys Chem 27:87CrossRefGoogle Scholar
- 48.Berlin YA, Grozema FC, Siebbeles LDA, Ratner MA (2008) J Phys Chem C 112:10988CrossRefGoogle Scholar
- 49.Tant J, Geerts YH, Lehmann M, Cupere VD, Zucchi G, Laursen BW, Bjornholm T, Lemaur V, Marcq V, Burquel A, Hennebicq E, Gardebien F, Viville P, Beljonne D, Lazzaroni R, Cornil J (2005) J Chem Phys B 109:20315CrossRefGoogle Scholar
- 50.Chang YC, Chao I (2010) J Phys Chem Lett 1:116CrossRefGoogle Scholar
- 51.Seo D, Hoffmann R (1999) Theor Chem Acc 102:23Google Scholar
- 52.Mikołajczyk M, Zaleśny R, Czyżnikowska Z, Bartkowiak W, Toman P, Leszczynski J (2009). Unpublished resultsGoogle Scholar
- 53.Stradomska A, Petelenz P (2006) Mol Phys 104:2063CrossRefGoogle Scholar
- 54.Petelenz P (2004) Org Electron 5:115CrossRefGoogle Scholar