Structural Deformation Controls Charge Losses in MAPbI3: Unsupervised Machine Learning of Nonadiabatic Molecular Dynamics,ACS Energy Letters

当前位置： X-MOL 学术 › ACS Energy Lett. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Structural Deformation Controls Charge Losses in MAPbI3: Unsupervised Machine Learning of Nonadiabatic Molecular Dynamics
ACS Energy Letters ( IF 19.3 ) Pub Date : 2020-05-15 , DOI: 10.1021/acsenergylett.0c00899
Guoqing Zhou ₁ , Weibin Chu ₂ , Oleg V. Prezhdo _{1,

2}

Affiliation

The rapid increase in perovskite solar cell efficiencies has motivated massive experimental and theoretical efforts aimed at understanding and enhancing the performance. We apply machine learning to nonadiabatic molecular dynamics simulation of nonradiative charge recombination in MAPbI₃ and discover that the I–I–I angle is the key structural parameter governing nonadiabatic electron–phonon coupling and the bandgap. Surprisingly, the structure of MAPbI₃ is much more important that the motions of MAPbI₃, even though the coupling depends explicitly on nuclear velocity. Also surprisingly, rotational and center-of-mass motions of MA influence charge recombination, even though MA does not contribute to electron or hole wave functions. The findings rationalize the unusual temperature dependence of carrier lifetimes in halide perovskites and emphasize inorganic lattice deformation and MA rotation during polaron formation. By detecting nontrivial correlations within complex data and providing accurate quantitative measures, machine learning surpasses traditional analyses and suggests that perovskite performance can be controlled by chemical changes that alter perovskite geometric structure.

中文翻译：

结构变形控制MAPbI _3中的电荷损失：非绝热分子动力学的无监督机器学习

钙钛矿太阳能电池效率的迅速提高激发了旨在理解和提高性能的大量实验和理论努力。我们将机器学习应用于MAPbI ₃中非辐射电荷复合的非绝热分子动力学模拟，发现I–I–I角是控制非绝热电子-声子耦合和带隙的关键结构参数。出人意料的是，MAPbI结构₃是更为重要的是MAPbI的运动₃，即使耦合明确取决于核速度。同样出人意料的是，即使MA对电子或空穴波功能没有贡献，MA的旋转运动和质心运动也会影响电荷复合。这些发现合理化了卤化物钙钛矿中载流子寿命的异常温度依赖性，并强调了极化子形成过程中无机晶格变形和MA旋转。通过检测复杂数据中的非平凡相关性并提供准确的定量度量，机器学习超越了传统分析方法，并表明钙钛矿的性能可以通过改变钙钛矿几何结构的化学变化来控制。

更新日期：2020-05-15

点击分享查看原文

点击收藏

阅读更多本刊新发论文本刊介绍/投稿指南