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物理化学学报  2018, Vol. 34 Issue (10): 1106-1115    DOI: 10.3866/PKU.WHXB201701083
所属专题: 材料科学的分子模拟
论文     
Efficient Calculation of Absorption Spectra in Solution: Approaches for Selecting Representative Solvent Configurations and for Reducing the Number of Explicit Solvent Molecules
XUE Bai1,CHEN Tiannan1,,SIEPMANN J.Ilja1,2,*()
1 Department of Chemistry and Chemical Theory Center, University of Minnesota, 207 Pleasant Street SE, Minneapolis, MN 55455-0240, USA
2 Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN 55455-0132, USA
? Current address: Department of Computer Science and Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, MN 55455-0154, USA
Efficient Calculation of Absorption Spectra in Solution: Approaches for Selecting Representative Solvent Configurations and for Reducing the Number of Explicit Solvent Molecules
Bai XUE1,Tiannan CHEN1,,J. Ilja SIEPMANN1,2,*()
1 Department of Chemistry and Chemical Theory Center, University of Minnesota, 207 Pleasant Street SE, Minneapolis, MN 55455-0240, USA
2 Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN 55455-0132, USA
? Current address: Department of Computer Science and Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, MN 55455-0154, USA
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摘要:

Dye-sensitized solar cells (DSSCs) are one of the most promising renewable energy technologies. Charge transfer and charge transport are pivotal processes in DSSCs, which govern solar energy capture and conversion. These processes can be probed using modern electronic structure methods. Because of the heterogeneity and complexity of the local environment of a chromophore in DSSCs (such as solvatochromism and chromophore aggregation), a part of the solvation environment should be treated explicitly during the calculation. However, because of the high computational cost and unfavorable scaling with the number of electrons of high-level quantum mechanical methods, approaches to explicitly treat the local environment need careful consideration. Two problems must be tackled to reduce computational cost. First, the number of configurations representing the solvent distribution should be limited as much as possible. Second, the size of the explicit region should be kept relatively small. The purpose of this study is to develop efficient computational approaches to select representative configurations and to limit the explicit solvent region to reduce the computational cost for later (higher-level) quantum mechanical calculations. For this purpose, an ensemble of solvent configurations around a 1-methyl-8-oxyquinolinium betaine (QB) dye molecule was generated using Monte Carlo simulations and molecular mechanics force fields. Then, a fitness function was developed using data from inexpensive electronic structure calculations to reduce the number of configurations. Specific solvent molecules were also selected for explicit treatment based on a distance criterion, and those not selected were treated as background charges. The configurations and solvent molecules selected proved to be good representatives of the entire ensemble; thus, expensive electronic structure calculations need to be performed only on this subset of the system, which significantly reduces the computational cost.

关键词: Monte Carlo simulationChromophoreSpectraSolution    
Abstract:

Dye-sensitized solar cells (DSSCs) are one of the most promising renewable energy technologies. Charge transfer and charge transport are pivotal processes in DSSCs, which govern solar energy capture and conversion. These processes can be probed using modern electronic structure methods. Because of the heterogeneity and complexity of the local environment of a chromophore in DSSCs (such as solvatochromism and chromophore aggregation), a part of the solvation environment should be treated explicitly during the calculation. However, because of the high computational cost and unfavorable scaling with the number of electrons of high-level quantum mechanical methods, approaches to explicitly treat the local environment need careful consideration. Two problems must be tackled to reduce computational cost. First, the number of configurations representing the solvent distribution should be limited as much as possible. Second, the size of the explicit region should be kept relatively small. The purpose of this study is to develop efficient computational approaches to select representative configurations and to limit the explicit solvent region to reduce the computational cost for later (higher-level) quantum mechanical calculations. For this purpose, an ensemble of solvent configurations around a 1-methyl-8-oxyquinolinium betaine (QB) dye molecule was generated using Monte Carlo simulations and molecular mechanics force fields. Then, a fitness function was developed using data from inexpensive electronic structure calculations to reduce the number of configurations. Specific solvent molecules were also selected for explicit treatment based on a distance criterion, and those not selected were treated as background charges. The configurations and solvent molecules selected proved to be good representatives of the entire ensemble; thus, expensive electronic structure calculations need to be performed only on this subset of the system, which significantly reduces the computational cost.

Key words: Monte Carlo simulation    Chromophore    Spectra    Solution
收稿日期: 2017-12-14 出版日期: 2018-04-13
通讯作者: SIEPMANN J.Ilja     E-mail: siepmann@umn.edu
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XUE Bai,CHEN Tiannan,SIEPMANN J.Ilja. Efficient Calculation of Absorption Spectra in Solution: Approaches for Selecting Representative Solvent Configurations and for Reducing the Number of Explicit Solvent Molecules[J]. 物理化学学报, 2018, 34(10): 1106-1115, 10.3866/PKU.WHXB201701083

Bai XUE,Tiannan CHEN,J. Ilja SIEPMANN. Efficient Calculation of Absorption Spectra in Solution: Approaches for Selecting Representative Solvent Configurations and for Reducing the Number of Explicit Solvent Molecules. Acta Phys. -Chim. Sin., 2018, 34(10): 1106-1115, 10.3866/PKU.WHXB201701083.

链接本文:

http://www.whxb.pku.edu.cn/CN/10.3866/PKU.WHXB201701083        http://www.whxb.pku.edu.cn/CN/Y2018/V34/I10/1106

Fig 1  (left) Structure of QB and atom numbering where cyan, white, red, and blue spheres indicate carbon, hydrogen, oxygen, and nitrogen atoms, respectively; (right) chemical structure of QB and ground state (GS) and excited state (ES) partial charges of oxygen and nitrogen atoms calculated at the TD-CAM-B3LYP/6-311+G(d, p) level of theory.
Fig 2  (top) O(QB)-O(water) radial distribution function (RDF, left) and corresponding number integral (nint, right); (bottom) O(QB)-Me(ACN) radial distribution function (RDF, left) and corresponding number integral (nint, right) (1 Å = 0.1 nm).
Fig 3  ZINDO excitation energy spectra of the QB chromophore in water and acetonitrile based on 1600 configurations (with a Lorentzian broadening of γ = 0.005).
Solvent VCoul, GS VCoul, ES
water 0.739 0.346
ACN 0.715 0.103
Table 1  Correlation coefficients (R) between Epred and EZINDO when using VCoul, GS and VCoul, ES
Fig 4  Correlation between Epred and EZINDO using fitness functions based on VCoul, GS and VCoul, ES. Only the training sets are shown here, and the red lines denote the linear regression lines.
Solvent R β1 β2 Eg, ZINDO β1/β2
water 0.875 ?8.52 8.20 1.77 ?1.04
ACN 0.959 ?12.1 11.6 1.77 ?1.05
water 0.875 ?10.3 9.90 1.77 ?1.04
ACN 0.953 ?10.3 9.90 1.77 ?1.04
Table 2  Correlation and coefficients of fitness function based on both VCoul, GS and VCoul, ES.
Fig 5  Correlation between Epred and EZINDO using the fitness function combining VCoul, GS and VCoul, ES for solvent water and acetonitrile. (top) solvation in water (left) and acetonitrile (right) with β1 and β2 obtained from the regression method for a specific solvent; (bottom) solvation in water (left) and acetonitrile with universal β1 and β2 values.
Fig 6  D and D* for two biased selection schemes based on predicted and pre-known excitation energies, and Dbiased/Drand. Uncertainties are only shown when they are larger than the symbol size.
Solvent R β1 β2 Eg, TD-DFT β1/β2
water 0.918 ?10.3 9.90 2.05 ?1.04
ACN 0.887 ?10.3 9.90 2.05 ?1.04
water 0.926 use Eq. (8)
ACN 0.898 use Eq. (8)
Table 3  Correlation and coefficients of fitness function based on selected solvent molecules.
Fig 7  Average ZINDO excitation energies of selected configurations and uncertainties. top: water; bottom: acetonitrile. Dashed lines denote the averages for the full 1600 configurations.
Fig 8  Correlation between Epred and ETD-DFT for water and acetonitrile using Eqs. (6) and (8).
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