Fan Jinming, Yuan Bowei, Qian Chao, Zhou Shaodong
College of Chemical and Biological Engineering, Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology, Zhejiang University, Hangzhou 310027, P. R. China.
Zhejiang Provincial Innovation Center of Advanced Chemicals Technology, Institute of Zhejiang University-Quzhou, Quzhou 324000, P. R. China.
Precis Chem. 2024 Apr 8;2(6):263-272. doi: 10.1021/prechem.4c00015. eCollection 2024 Jun 24.
To rationalize the design of D-π-A type organic small-molecule nonlinear optical materials, a theory guided machine learning framework is constructed. Such an approach is based on the recognition that the optical property of the molecule is predictable upon accumulating the contribution of each component, which is in line with the concept of group contribution method in thermodynamics. To realize this, a Lewis-mode group contribution method (LGC) has been developed in this work, which is combined with the multistage Bayesian neural network and the evolutionary algorithm to constitute an interactive framework (LGC-msBNN-EA). Thus, different optical properties of molecules are afforded accurately and efficiently-by using only a small data set for training. Moreover, by employing the EA model designed specifically for LGC, structural search is well achievable. The origins of the satisfying performance of the framework are discussed in detail. Considering that such a framework combines chemical principles and data-driven tools, most likely, it will be proven to be rational and efficient to complete mission regarding structure design in related fields.
为了使D-π-A型有机小分子非线性光学材料的设计合理化，构建了一个理论指导的机器学习框架。这种方法基于这样一种认识，即分子的光学性质在累积每个组分的贡献时是可预测的，这与热力学中的基团贡献法概念一致。为了实现这一点，本文开发了一种Lewis模式基团贡献法（LGC），它与多级贝叶斯神经网络和进化算法相结合，构成了一个交互式框架（LGC-msBNN-EA）。因此，仅使用一个小数据集进行训练，就能准确、高效地获得分子的不同光学性质。此外，通过采用专门为LGC设计的EA模型，可以很好地实现结构搜索。详细讨论了该框架令人满意的性能的来源。考虑到这样一个框架结合了化学原理和数据驱动工具，很可能，它将被证明在完成相关领域的结构设计任务方面是合理且高效的。
