Coarse-Graining Organic Semiconductors: The Path to Multiscale Design
Coarse-Graining Organic Semiconductors: The Path to Multiscale Design
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DOI：
10.1021/acs.jpcb.0c09749
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发表时间：
2021-01-21
影响因子：
3.3
通讯作者：
Jackson, Nicholas E.
中科院分区：
文献类型：
--
作者：
Jackson, Nicholas E.
四十年的分子理论和计算帮助形成了对有机半导体物理化学的现代理解。虽然这些努力历来集中在单分子或二聚体尺度的电子结构表征上，但非晶体分子和聚合物半导体的新兴趋势正在推动对能够在中尺度上进行形态和电子结构预测的建模技术的需求。考虑到与这些预测任务相关的挑战，社区已经开始发展有机半导体的计算工具包，该工具包结合了软物质、粗粒度和机器学习领域的技术。在这里，我们重点介绍了针对非晶有机半导体的多尺度表征的粗粒度方法的最新进展。由于有机半导体的性能取决于中尺度形态和分子电子结构的相互作用，因此特别强调能够进行结构和电子预测的粗粒度建模方法，而无需求助于全原子表示。
Four decades of molecular theory and computation have helped form the modern understanding of the physical chemistry of organic semiconductors. Whereas these efforts have historically centered around characterizations of electronic structure at the single-molecule or dimer scale, emerging trends in noncrystalline molecular and polymeric semiconductors are motivating the need for modeling techniques capable of morphological and electronic structure predictions at the mesoscale. Provided the challenges associated with these prediction tasks, the community has begun to evolve a computational toolkit for organic semiconductors incorporating techniques from the fields of soft matter, coarse-graining, and machine learning. Here, we highlight recent advances in coarse-grained methodologies aimed at the multiscale characterization of noncrystalline organic semiconductors. As organic semiconductor performance is dependent on the interplay of mesoscale morphology and molecular electronic structure, specific emphasis is placed on coarse-grained modeling approaches capable of both structural and electronic predictions without recourse to all-atom representations.