Neural Network Driven, Interactive Design for Nonlinear Optical
  Molecules Based on Group Contribution Method
Jinming Fan Chao Qian Shaodong Zhou
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.