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Proceedings of the Online school on Fundamentals of Semiconductive Quantum Dots最新文献

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Computational Chemistry for Colloidal Semiconductor Nanocrystals 胶体半导体纳米晶体的计算化学
Pub Date : 2021-04-30 DOI: 10.29363/NANOGE.QDSSCHOOL.2021.013
I. Infante
: While experimental protocols aimed at the synthesis and characterization of colloidal semiconductor nanocrystals are now well established, theoretical calculations of the same materials still present many challenges. Some of these are: (1) the compromise in the size of the modelled systems, which have to be large enough to avoid too strong quantum confinement effects but small enough to be described computationally; (2) the construction of a nanocrystal model system that represent closely that found in the experiment, and (3) the possibility of adding surface ligands that increase dramatically the computational requirements. A successful methodology to tackle some of the above issues is Density Functional Theory (DFT), which scales cubically with the number of atoms, but that in recent years was capable to provide important insights in the electronic structure and geometry of several colloidal nanocrystals of up to 3nm in size. However, a strong limitation of DFT is the high computational cost, which prevent on the one hand the inclusion of solvent and ligand molecules as in the experiments and on the other, performing long timescale simulations to study rare events like ligand binding at the surface, trap formation rates and phonon induced non-radiative quenching. To address these chemico-physical processes, the field moved to computationally cheaper alternatives such as those based on classical force fields. These methods however have also important drawbacks, such as the development of accurate force-field parameters and more importantly the lack of information on the electronic structure of the material studied. In this framework, novel machine-learning techniques are poised to bridge the gap between DFT and classical force fields to enable DFT quality simulations on colloidal nanocrystals also for long timescale events. In this talk I will therefore discuss the most important achievements obtained in the semiconductor nanocrystal field by theoretical methodologies and an outlook of future developments.
虽然针对胶体半导体纳米晶体的合成和表征的实验方案现已建立,但相同材料的理论计算仍然存在许多挑战。其中一些是:(1)模型系统的大小折衷,必须足够大以避免太强的量子限制效应,但必须足够小以进行计算描述;(2)构建一个与实验中发现的纳米晶体模型系统;(3)添加表面配体的可能性,这大大增加了计算需求。密度泛函理论(DFT)是解决上述一些问题的一种成功方法,它与原子数量成三次方的比例,但近年来,它能够提供一些高达3nm尺寸的胶体纳米晶体的电子结构和几何形状的重要见解。然而,DFT的一个很强的局限性是高昂的计算成本,这一方面阻碍了实验中溶剂和配体分子的包合,另一方面,进行长时间尺度的模拟来研究配体表面结合、陷阱形成速率和声子诱导的非辐射猝灭等罕见事件。为了解决这些化学物理过程,该领域转向了计算成本更低的替代方案,例如基于经典力场的替代方案。然而,这些方法也有重要的缺点,如开发准确的力场参数,更重要的是缺乏关于所研究材料的电子结构的信息。在这个框架中,新的机器学习技术有望弥合DFT和经典力场之间的差距,使胶体纳米晶体上的DFT质量模拟也能用于长时间尺度事件。因此,在这次演讲中,我将讨论在半导体纳米晶体领域从理论方法和未来发展的展望方面取得的最重要的成就。
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引用次数: 0
In-situ Fabricated Perovskite Quantum Dots:From Synthesis to Applications 原位制备钙钛矿量子点:从合成到应用
Pub Date : 2021-04-30 DOI: 10.29363/NANOGE.QDSSCHOOL.2021.009
Haizheng Zhong
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引用次数: 0
Prospects and Challenges of Colloidal Quantum Dot Laser Diodes 胶体量子点激光二极管的前景与挑战
Pub Date : 1900-01-01 DOI: 10.29363/nanoge.qdsschool.2021.006
Victor I. Klimov
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引用次数: 4
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Proceedings of the Online school on Fundamentals of Semiconductive Quantum Dots
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