Electronic structure and non-linear optical properties of organic photovoltaic systems with potential applications on solar cell devices: A DFT approach
The use of eco-friendly materials for the environment has been addressed as a critical issue in the development of systems for renewable energy applications. In this regard, the investigation of organic photovoltaic (OPV) molecules for the implementation in solar cells, has become a subject of intense research in the last years. The present work is a systematic study at the B3LYP level of theory performed for a series of 50 OPV materials. Full geometry optimizations revealed that those systems with a twisted geometry are the most energetically stable. Nuclear independent Chemical shifts (NICS) values show a strong aromatic character along the series, indicating a possible polymerization in solid-state, via a π-π stacking, which may be relevant in the design of a solar cell device. The absorption spectra in the series was also computed using Time Dependent DFT at the same level of theory, indicating that all spectra are red-shifted along the series. This is a promissory property that may be directly implemented in a photovoltaic material, since it is possible to absorb a larger range of visible light. The computed HOMO-LUMO gaps as a measurement of the band gap in semiconductors, show a reasonable agreement with those found in experiment, predicting candidate materials that may be directly used in photovoltaic applications. Non-linear optical (NLO) properties were also estimated with the aid of a PCBM molecule as a model of an acceptor, and a final set of optimal systems was identified as potential candidates to be implemented as photovoltaic materials. The methodological approach presented in this work may aid in the in silico assisted-design of OPV materials.
Rapid Computational Optimization of Molecular Properties using Genetic Algorithms: Searching Across Millions of Compounds for Organic Photovoltaic Materials
Delocalisation explains efficient transport and charge generation in neat Y6 organic photovoltaics
University of Sydney（悉尼大学）；Victoria University of Wellington（惠灵顿维多利亚大学）；MacDiarmid Institute for Advanced Materials and Nanotechnology（先进材料与纳米技术麦克迪亚米德研究所）；The Dodd-Walls Centre for Photonic and Quantum Technologies（光子与量子技术多兹中心）；Institute for Integrated Cell-Material Sciences (iCeMS)（细胞材料综合科学研究所）
Density-Functional Theory (DFT) and Time-Dependent DFT Study of the Chemical and Physical Origins of Key Photoproperties of End-Group Derivatives of the Nonfullerene Bulk Heterojunction Organic Solar Cell Acceptor Molecule IDIC
GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices
Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校西贝尔计算与数据科学学院）；Department of Chemical & Biomolecular Engineering, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校化学与生物分子工程系）
Temperature-Driven Sequential Modeling for the Prediction of Annual Power Conversion Efficiency Profiles of Organic Photovoltaic Materials: Douala Case Study
University of Douala（杜阿拉大学）；Stellenbosch University（斯泰伦博斯大学）；University of Quebec in Abitibi-Témiscamingue（阿比蒂比-蒂米斯坎明格魁北克大学）；University of Yaounde 1（雅温得第一大学）
Excited-state potential-energy surfaces of metal-adsorbed organic molecules from Linear Expansion Δ-Self-Consistent Field Density-Functional Theory (ΔSCF-DFT)
准确、高效地模拟金属表面的激发态性质是研究金属表面吸附动力学的重要基础。为此，Gavnholt等人最近提出的线性展开Δ自洽场（leΔSCF）方法[Phys. Rev. B 78，075441（2008）]为耗时的准粒子计算提供了一种有效的替代方案。在该方法中，密度泛函理论的标准Kohn-Sham方程在类似于吸附质的气相轨道的Hilbert空间区域中的非平衡占据的约束下求解。在这项工作中，我们讨论了这种方法的适用性金属表面安装的有机吸附物的激发态动力学，特别是在分子开关的背景下。我们提出了必要的进步，以允许激发态势能面（PES）的质量一致的描述，并说明了与应用程序的概念，偶氮苯吸附在Ag（111）和Au（111）表面。我们发现，明确列入基板电子状态修改的拓扑结构的分子内激发态PES的分子，由于图像电荷和杂化效应。虽然气相中的分子显示出诱导异构化和逆反应的共振的清晰的能量分离，但表面吸附的分子没有。伴随的可能同时诱导两个过程将导致这种机制的转换效率显著降低。
Machine Learning for Screening Large Organic Molecules
有机半导体是廉价、可扩展和可持续的电子器件、发光二极管和光电子器件的有前途的材料。对于有机光电池来说，在广阔的化合物空间中寻找具有合适性质的化合物是一个挑战。例如，电离能应该适合太阳光的光谱，并且能级必须允许有效的电荷传输。在这里，开发了一种机器学习模型，用于快速准确地估计给定分子结构的HOMO和LUMO能量。它建立在SchNet模型（Schütt et al.（2018））的基础上，并增加了“Set 2Set”读出模块（Vinyals et al.（2016））。Set 2Set模块比求和和平均聚合具有更强的表达能力，更适合于所考虑的复杂量。大多数以前的模型都是在相当小的分子上训练和评估的。因此，第二个贡献是通过添加来自其他来源的更大分子并建立一致的训练/验证/测试分割来扩展机器学习方法的范围。作为第三个贡献，我们做了一个多任务分析，以解决来自不同理论层次的不同来源的问题。所有这三个贡献结合起来，使模型的准确性接近化学准确性。
