Charge Transfer into Organic Thin Films: A Deeper Insight through Machine-Learning-Assisted Structure Search
Charge Transfer into Organic Thin Films: A Deeper Insight through Machine-Learning-Assisted Structure Search
复制标题
有机薄膜中的电荷转移：通过机器学习辅助结构搜索的更深层次的洞察
DOI：
10.1002/advs.202000992
复制
发表时间：
2020-06-28
期刊：
影响因子：
15.1
通讯作者：
Hofmann, Oliver T.
中科院分区：
文献类型：
--
作者：
Egger, Alexander T.;Hoermann, Lukas;Hofmann, Oliver T.
密度泛函理论计算与机器学习相结合，研究四氰乙烯/Cu(111)杂化有机/无机界面处的覆盖率依赖性电荷转移。该研究发现了两种不同的单层相，它们表现出性质不同的电荷转移行为。我们的结果反驳了先前关于长程电荷转移到不与表面直接接触的分子的理论。相反，他们证明实验证据支持我们关于第一单层的覆盖依赖性结构重新定向的假设。界面处的这种相变可能比目前想象的更为常见，这需要对有机/无机界面进行彻底的重新评估。
Density functional theory calculations are combined with machine learning to investigate the coverage-dependent charge transfer at the tetracyanoethylene/Cu(111) hybrid organic/inorganic interface. The study finds two different monolayer phases, which exhibit a qualitatively different charge-transfer behavior. Our results refute previous theories of long-range charge transfer to molecules not in direct contact with the surface. Instead, they demonstrate that experimental evidence supports our hypothesis of a coverage-dependent structural reorientation of the first monolayer. Such phase transitions at interfaces may be more common than currently envisioned, beckoning a thorough reevaluation of organic/inorganic interfaces.