A Lewis-mode group contribution method (LGC) -- multi-stage Bayesian neural
network (msBNN) -- evolutionary algorithm (EA) framework is reported for
rational design of D-Pi-A type organic small-molecule nonlinear optical
materials is presented. Upon combination of msBNN and corrected Lewis-mode
group contribution method (cLGC), 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 logical origins of the well
performance of the framework are discussed in detail. Considering that such a
theory guided, machine learning framework combines chemical principles and
data-driven tools, most likely, it will be proven efficient to solve molecular
design related problems in wider fields.

使用 Lewis - 模态群贡献方法（LGC）、多阶段贝叶斯神经网络（msBNN）和进化算法（EA）框架进行 D-Pi-A 类型有机小分子非线性光学材料的有理设计。通过结合 msBNN 和修正的 LGC 方法（cLGC），仅利用小规模数据集，准确高效地获得分子的不同光学性质。此外，利用为 LGC 专门设计的 EA 模型，可以有效进行结构搜索。详细讨论了该框架性能良好的逻辑起源。考虑到这种理论引导的机器学习框架结合了化学原理和数据驱动工具，很可能在更广泛的领域中高效解决分子设计相关问题。