Group Contribution Method Supervised Neural Network for Precise Design of Organic Nonlinear Optical Materials

Abstract

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.