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Computationally assisted multistage design and prediction driving the discovery of deep-ultraviolet nonlinear optical materials

机译:计算辅助设计和多级预测驱动的发现deep-ultraviolet非线性光学材料

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摘要

With the rapid development of computer technology, material design and crystal-structural prediction based on density functional theory have become one "top-down" strategy to accelerate the process of advanced materials discovery. In this review, we outline several examples for material design and prediction which drive the discovery of deep-ultraviolet nonlinear optical materials. The computer-aided material design and prediction platform including the high-throughput screening systems is discussed. The property-driven materials multistage design process from functional groups, module to functional layer analysis and structural prediction is described to explore new deep-ultraviolet nonlinear optical materials. Finally, there are still grand challenges in computer-aided design and prediction, and it is hoped that the computer-aided strategy will provide a more powerful ability on the most promising nonlinear optical candidates and related fields.
机译:随着计算机技术的快速发展,材料设计和晶体结构预测基于密度泛函理论一个“自上而下”的策略来加速这个过程先进材料的发现。我们为材料设计大纲几个例子和预测哪个驱动器的发现deep-ultraviolet非线性光学材料。计算机辅助材料设计和预测平台包括大规模筛选系统进行了探讨。材料的多级设计过程官能团,模块功能层分析和结构预测探索新的deep-ultraviolet非线性光学材料。在计算机辅助设计和挑战预测和希望计算机辅助策略将提供一个更最有前途的非线性的强大能力光学候选人和相关领域。

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