Fast and Accurate Artificial Neural Network Potential Model for MAPbI₃ Perovskite Materials

Hybrid organic–inorganic perovskite materials are promising materials for photovoltaic and optoelectronic applications. Nevertheless, the construction of a computationally efficient potential model for atomistic simulations of perovskite with high fidelity to ab initio calculations is not a trivial task given the chemically complex nature of perovskite in terms of its chemical components and interatomic interactions. In the present study, we demonstrate that artificial neural network (ANN) models can be employed for efficient and accurate potential energy evaluation of MAPbI3 perovskite materials. The ANN models were trained using training sets composed of thousands of atomic images of tetragonal MAPbI3 crystals, with their respective energies and atomic forces obtained from ab initio calculations. The trained ANN models were validated by predicting the lattice parameters and energies/atomic forces of cubic MAPbI3 perovskite and had excellent agreement with ab initio calculations. The phonon modes could also be extracted using the trained ANN model with good agreement with ab initio calculations, provided that the atomic forces were incorporated into the training processes. Finally, we demonstrate that for a given system size, the trained ANN model offers 104 to 105 faster time consumption per energy evaluation relative to ab initio calculations using Vienna Ab initio Simulation Package, demonstrating the potential of the ANN model for exhaustively sampling the configuration spaces of chemically complex materials for predictions of thermodynamic properties and phase stabilities

Fast and Accurate Artificial Neural Network Potential Model for MAPbI₃ Perovskite Materials

Hybrid organic–inorganic perovskite materials are promising materials for photovoltaic and optoelectronic applications. Nevertheless, the construction of a computationally efficient potential model for atomistic simulations of perovskite with high fidelity to ab initio calculations is not a trivial task given the chemically complex nature of perovskite in terms of its chemical components and interatomic interactions. In the present study, we demonstrate that artificial neural network (ANN) models can be employed for efficient and accurate potential energy evaluation of MAPbI3 perovskite materials. The ANN models were trained using training sets composed of thousands of atomic images of tetragonal MAPbI3 crystals, with their respective energies and atomic forces obtained from ab initio calculations. The trained ANN models were validated by predicting the lattice parameters and energies/atomic forces of cubic MAPbI3 perovskite and had excellent agreement with ab initio calculations. The phonon modes could also be extracted using the trained ANN model with good agreement with ab initio calculations, provided that the atomic forces were incorporated into the training processes. Finally, we demonstrate that for a given system size, the trained ANN model offers 104 to 105 faster time consumption per energy evaluation relative to ab initio calculations using Vienna Ab initio Simulation Package, demonstrating the potential of the ANN model for exhaustively sampling the configuration spaces of chemically complex materials for predictions of thermodynamic properties and phase stabilities

Multiscale Molecular Simulation of Solution Processing of SMDPPEH: PCBM Small-Molecule Organic Solar Cells

Solution-processed small-molecule organic solar cells are a promising renewable energy source because of their low production cost, mechanical flexibility, and light weight relative to their pure inorganic counterparts. In this work, we developed a coarse-grained (CG) Gay–Berne ellipsoid molecular simulation model based on atomistic trajectories from all-atom molecular dynamics simulations of smaller system sizes to systematically study the nanomorphology of the SMDPPEH/PCBM/solvent ternary blend during solution processing, including the blade-coating process by applying external shear to the solution. With the significantly reduced overall system degrees of freedom and computational acceleration from GPU, we were able to go well beyond the limitation of conventional all-atom molecular simulations with a system size on the order of hundreds of nanometers with mesoscale molecular detail. Our simulations indicate that, similar to polymer solar cells, the optimal blending ratio in small-molecule organic solar cells must provide the highest specific interfacial area for efficient exciton dissociation, while retaining balanced hole/electron transport pathway percolation. We also reveal that blade-coating processes have a significant impact on nanomorphology. For given donor/acceptor blending ratios, applying an external shear force can effectively promote donor/acceptor phase segregation and stacking in the SMDPPEH domains. The present study demonstrated the capability of an ellipsoid-based coarse-grained model for studying the nanomorphology evolution of small-molecule organic solar cells during solution processing/blade-coating and provided links between fabrication protocols and device nanomorphologies

Fast and Accurate Artificial Neural Network Potential Model for MAPbI₃ Perovskite Materials

Hybrid organic–inorganic perovskite materials are promising materials for photovoltaic and optoelectronic applications. Nevertheless, the construction of a computationally efficient potential model for atomistic simulations of perovskite with high fidelity to ab initio calculations is not a trivial task given the chemically complex nature of perovskite in terms of its chemical components and interatomic interactions. In the present study, we demonstrate that artificial neural network (ANN) models can be employed for efficient and accurate potential energy evaluation of MAPbI3 perovskite materials. The ANN models were trained using training sets composed of thousands of atomic images of tetragonal MAPbI3 crystals, with their respective energies and atomic forces obtained from ab initio calculations. The trained ANN models were validated by predicting the lattice parameters and energies/atomic forces of cubic MAPbI3 perovskite and had excellent agreement with ab initio calculations. The phonon modes could also be extracted using the trained ANN model with good agreement with ab initio calculations, provided that the atomic forces were incorporated into the training processes. Finally, we demonstrate that for a given system size, the trained ANN model offers 104 to 105 faster time consumption per energy evaluation relative to ab initio calculations using Vienna Ab initio Simulation Package, demonstrating the potential of the ANN model for exhaustively sampling the configuration spaces of chemically complex materials for predictions of thermodynamic properties and phase stabilities

Fast and Accurate Artificial Neural Network Potential Model for MAPbI₃ Perovskite Materials

Hybrid organic–inorganic perovskite materials are promising materials for photovoltaic and optoelectronic applications. Nevertheless, the construction of a computationally efficient potential model for atomistic simulations of perovskite with high fidelity to ab initio calculations is not a trivial task given the chemically complex nature of perovskite in terms of its chemical components and interatomic interactions. In the present study, we demonstrate that artificial neural network (ANN) models can be employed for efficient and accurate potential energy evaluation of MAPbI3 perovskite materials. The ANN models were trained using training sets composed of thousands of atomic images of tetragonal MAPbI3 crystals, with their respective energies and atomic forces obtained from ab initio calculations. The trained ANN models were validated by predicting the lattice parameters and energies/atomic forces of cubic MAPbI3 perovskite and had excellent agreement with ab initio calculations. The phonon modes could also be extracted using the trained ANN model with good agreement with ab initio calculations, provided that the atomic forces were incorporated into the training processes. Finally, we demonstrate that for a given system size, the trained ANN model offers 104 to 105 faster time consumption per energy evaluation relative to ab initio calculations using Vienna Ab initio Simulation Package, demonstrating the potential of the ANN model for exhaustively sampling the configuration spaces of chemically complex materials for predictions of thermodynamic properties and phase stabilities

Fast and Accurate Artificial Neural Network Potential Model for MAPbI₃ Perovskite Materials

Hybrid organic–inorganic perovskite materials are promising materials for photovoltaic and optoelectronic applications. Nevertheless, the construction of a computationally efficient potential model for atomistic simulations of perovskite with high fidelity to ab initio calculations is not a trivial task given the chemically complex nature of perovskite in terms of its chemical components and interatomic interactions. In the present study, we demonstrate that artificial neural network (ANN) models can be employed for efficient and accurate potential energy evaluation of MAPbI3 perovskite materials. The ANN models were trained using training sets composed of thousands of atomic images of tetragonal MAPbI3 crystals, with their respective energies and atomic forces obtained from ab initio calculations. The trained ANN models were validated by predicting the lattice parameters and energies/atomic forces of cubic MAPbI3 perovskite and had excellent agreement with ab initio calculations. The phonon modes could also be extracted using the trained ANN model with good agreement with ab initio calculations, provided that the atomic forces were incorporated into the training processes. Finally, we demonstrate that for a given system size, the trained ANN model offers 104 to 105 faster time consumption per energy evaluation relative to ab initio calculations using Vienna Ab initio Simulation Package, demonstrating the potential of the ANN model for exhaustively sampling the configuration spaces of chemically complex materials for predictions of thermodynamic properties and phase stabilities

Solubility of [6,6]-Phenyl-C₆₁-butyric Acid Methyl Ester and Optimal Blending Ratio of Bulk Heterojunction Polymer Solar Cells

The nanoscale mechanism determining the optimal electron donor/acceptor blending ratios is not yet clear. In this study, we used coarse-grained molecular simulations to simulate the thermal annealing process of poly-2,5-bis­(3-tetradecylthiophene-2-yl)­thieno­[3,2-b]­thiophene (PBTTT):[6,6]-phenyl-C61-butyric acid methyl ester (PCBM) blends to reveal the correlation between solubility of PCBM in electron donor materials and the optimal electron donor/acceptor blending ratio of the bulk heterojunction polymer solar cells. Substantial intercalation of PCBM into PBTTT is observed, and an interpenetrating network comprising two phasesPBTTT-plus-PCBM and pure PCBMis formed when the blending ratio is beyond 1:1. By comparing morphological properties of PBTTT:PCBM blends with those of the poly­(3-hexylthiophene) (P3HT):PCBM blend, a blend which we investigated earlier, we reveal that, in addition to specific interfacial area and percolation probabilities, the solubility of PCBM in PBTTT also has significant effects in determining the optimal blending ratio. Due to PCBM intercalation into PBTTT, more PCBM must be inserted for the precipitation of pure PCBM domains for electron transport. Herein, we provide insight into the effects of PCBM solubility and the nanoscale mechanisms determining the optimal blending ratios and demonstrate how multiscale simulation can potentially aid the development of novel bulk heterojunction blends

Nanomorphology Evolution of P3HT/PCBM Blends during Solution-Processing from Coarse-Grained Molecular Simulations

Solvent screening is a critical aspect of the nanomorphological control of low-cost, all-solution-processed bulk heterojunction organic photovoltaic cells. In order to reveal the correlations between solvent/solvent mixtures and the bulk heterojunction nanomorphologies during solution-processing, we constructed a multiscale, coarse-grained molecular simulation model for ternary solvent/P3HT/PCBM mixtures to systematically investigate the nanomorphologies of P3HT/PCBM blends during solution-processing in solutions over a wide range of P3HT/PCBM solubilities and solvent evaporation rates experienced during spin-casting processes. The resulting bulk heterojunction layer morphologies of the dried films were in good agreement with available experimental results from as-spun films, which validates our coarse-grained model. Our simulations indicated that the bulk heterojunction morphologies formed in solution involve a complicated interplay among the affinities of the solvent, P3HT, and PCBM in the ternary system, as well as the solubilities of the donor and acceptor; in particular, the solubility of the less-mobile material (i.e., P3HT) can notably affect the film quality, compactness, and the degree of donor/acceptor domain fineness in the dried films. Therefore, the present study demonstrates that this multiscale molecular simulation model can be used to accurately investigate the morphological evolution of bulk heterojunction blends during solution-processing and can be readily applied to the modeling of other advanced all-solution-processed organic photovoltaic cells such as small-molecule bulk heterojunction organic photovoltaic cells

Solubility of [6,6]-Phenyl-C₆₁-butyric Acid Methyl Ester and Optimal Blending Ratio of Bulk Heterojunction Polymer Solar Cells

The nanoscale mechanism determining the optimal electron donor/acceptor blending ratios is not yet clear. In this study, we used coarse-grained molecular simulations to simulate the thermal annealing process of poly-2,5-bis­(3-tetradecylthiophene-2-yl)­thieno­[3,2-<i>b</i>]­thiophene (PBTTT):[6,6]-phenyl-C₆₁-butyric acid methyl ester (PCBM) blends to reveal the correlation between solubility of PCBM in electron donor materials and the optimal electron donor/acceptor blending ratio of the bulk heterojunction polymer solar cells. Substantial intercalation of PCBM into PBTTT is observed, and an interpenetrating network comprising two phasesPBTTT-plus-PCBM and pure PCBMis formed when the blending ratio is beyond 1:1. By comparing morphological properties of PBTTT:PCBM blends with those of the poly­(3-hexylthiophene) (P3HT):PCBM blend, a blend which we investigated earlier, we reveal that, in addition to specific interfacial area and percolation probabilities, the solubility of PCBM in PBTTT also has significant effects in determining the optimal blending ratio. Due to PCBM intercalation into PBTTT, more PCBM must be inserted for the precipitation of pure PCBM domains for electron transport. Herein, we provide insight into the effects of PCBM solubility and the nanoscale mechanisms determining the optimal blending ratios and demonstrate how multiscale simulation can potentially aid the development of novel bulk heterojunction blends

Solubility of [6,6]-Phenyl-C₆₁-butyric Acid Methyl Ester and Optimal Blending Ratio of Bulk Heterojunction Polymer Solar Cells

The nanoscale mechanism determining the optimal electron donor/acceptor blending ratios is not yet clear. In this study, we used coarse-grained molecular simulations to simulate the thermal annealing process of poly-2,5-bis­(3-tetradecylthiophene-2-yl)­thieno­[3,2-<i>b</i>]­thiophene (PBTTT):[6,6]-phenyl-C₆₁-butyric acid methyl ester (PCBM) blends to reveal the correlation between solubility of PCBM in electron donor materials and the optimal electron donor/acceptor blending ratio of the bulk heterojunction polymer solar cells. Substantial intercalation of PCBM into PBTTT is observed, and an interpenetrating network comprising two phasesPBTTT-plus-PCBM and pure PCBMis formed when the blending ratio is beyond 1:1. By comparing morphological properties of PBTTT:PCBM blends with those of the poly­(3-hexylthiophene) (P3HT):PCBM blend, a blend which we investigated earlier, we reveal that, in addition to specific interfacial area and percolation probabilities, the solubility of PCBM in PBTTT also has significant effects in determining the optimal blending ratio. Due to PCBM intercalation into PBTTT, more PCBM must be inserted for the precipitation of pure PCBM domains for electron transport. Herein, we provide insight into the effects of PCBM solubility and the nanoscale mechanisms determining the optimal blending ratios and demonstrate how multiscale simulation can potentially aid the development of novel bulk heterojunction blends