数据清理和基于子单元的分子描述能够准确预测有机太阳能电池中使用的非富勒烯受体的能级,Advanced Science

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数据清理和基于子单元的分子描述能够准确预测有机太阳能电池中使用的非富勒烯受体的能级
Advanced Science ( IF 14.3 ) Pub Date : 2024-02-22 , DOI: 10.1002/advs.202308652
Ting Zhang ₁ , Joshua Yuk Lin Lai ₂ , Mingzhe Shi ₁ , Qing Li ₁ , Chen Zhang ₁ , He Yan ₂

Affiliation

非富勒烯受体（NFA）最近已成为提高有机太阳能电池（OSC）效率的关键材料。为了进一步提高 OSC 效率，必须精确控制 NFA 的能级，因此需要开发强大的计算方法来准确预测能级。不幸的是，在预测能级时，传统的计算技术通常会产生相对较大的误差，通常范围为 0.2 至 0.5 电子伏 (eV)。在这项研究中，作者提出了一种新方法，不仅可以加快能级预测，还可以显着提高准确性，将误差范围降低至 0.06 eV。该方法包括两个基本组成部分。第一个组成部分涉及数据清理，系统地消除有问题的实验数据，从而最大限度地减少输入数据错误。第二部分介绍了基于包含 NFA 的子单元的电子特性的分子描述方法。与传统的密度泛函理论（DFT）方法相比，该方法简化了分子计算的复杂性，并显示出显着增强的预测性能。我们的方法将加快 NFA 领域的研究，作为开发类似计算方法的催化剂，以应对材料科学和分子研究其他领域的挑战。

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Data Cleansing and Sub-Unit-Based Molecular Description Enable Accurate Prediction of The Energy Levels of Non-Fullerene Acceptors Used in Organic Solar Cells

Non-fullerene acceptors (NFAs) have recently emerged as pivotal materials for enhancing the efficiency of organic solar cells (OSCs). To further advance OSC efficiency, precise control over the energy levels of NFAs is imperative, necessitating the development of a robust computational method for accurate energy level predictions. Unfortunately, conventional computational techniques often yield relatively large errors, typically ranging from 0.2 to 0.5 electronvolts (eV), when predicting energy levels. In this study, the authors present a novel method that not only expedites energy level predictions but also significantly improves accuracy , reducing the error margin to 0.06 eV. The method comprises two essential components. The first component involves data cleansing, which systematically eliminates problematic experimental data and thereby minimizes input data errors. The second component introduces a molecular description method based on the electronic properties of the sub-units comprising NFAs. The approach simplifies the intricacies of molecular computation and demonstrates markedly enhanced prediction performance compared to the conventional density functional theory (DFT) method. Our methodology will expedite research in the field of NFAs, serving as a catalyst for the development of similar computational approaches to address challenges in other areas of material science and molecular research.

更新日期：2024-02-22

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