Determining interchromophore effects for energy transport in molecular networks using machine-learning algorithms.
Determining interchromophore effects for energy transport in molecular networks using machine-learning algorithms.
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DOI：
10.1039/d2cp04960k
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发表时间：
2023-02-01
影响因子：
3.3
通讯作者：
Melinger, Joseph S. S.
中科院分区：
文献类型：
--
作者：
Rolczynski, Brian S. S.;Diaz, Sebastian A.;Kim, Young C. C.;Mathur, Divita;Klein, William P. P.;Medintz, Igor L. L.;Melinger, Joseph S. S.
自然界使用具有高度优化的结构和能量特征的发色团网络来执行重要的化学功能。由于其模块化，可预测的聚集特性，并建立合成协议，结构DNA纳米技术是一个有前途的介质，用于安排生色团网络与类似的结构和能量控制。然而，这种高水平的控制产生了更大的需要知道如何精确地优化系统。本研究使用系统的模块化来产生耦合的14-位点发色团网络的变化。它使用机器学习算法和光谱测量来揭示这些站点的能量传输角色，特别关注它们在网络传输中相互施加的合作和抑制效应。这些模式的物理意义的背景下，使用分子动力学模拟和能量传输模型。该分析产生了关于能量如何在施主-中继器和中继器-受体接口之间传输以及通过同质中继器段的能量传输路径的见解。总的来说，这份报告建立了一种方法，使用机器学习方法来详细了解每个站点在光电分子网络中扮演的角色。
Nature uses chromophore networks, with highly optimized structural and energetic characteristics, to perform important chemical functions. Due to its modularity, predictable aggregation characteristics, and established synthetic protocols, structural DNA nanotechnology is a promising medium for arranging chromophore networks with analogous structural and energetic controls. However, this high level of control creates a greater need to know how to optimize the systems precisely. This study uses the system’s modularity to produce variations of a coupled 14-Site chromophore network. It uses machine-learning algorithms and spectroscopy measurements to reveal the energy-transport roles of these Sites, paying particular attention to the cooperative and inhibitive effects they impose on each other for transport across the network. The physical significance of these patterns is contextualized, using molecular dynamics simulations and energy-transport modeling. This analysis yields insights about how energy transfers across the Donor–Relay and Relay–Acceptor interfaces, as well as the energy-transport pathways through the homogeneous Relay segment. Overall, this report establishes an approach that uses machine-learning methods to understand, in fine detail, the role that each Site plays in an optoelectronic molecular network.
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