Deep Neural Networks for Multicomponent Molecular Systems.

ABSTRACT: Deep neural networks (DNNs) represent promising approaches to molecular machine learning (ML). However, their applicability remains limited to single-component materials and a general DNN model capable of handling various multicomponent molecular systems with composition data is still elusive, while current ML approaches for multicomponent molecular systems are still molecular descriptor-based. Here, a general DNN architecture extending existing molecular DNN models to multicomponent systems called MEIA is proposed. Case studies showed that the MEIA architecture could extend two exiting molecular DNN models to multicomponent systems with the same procedure, and that the obtained models that could learn both the molecular structure and composition information with equal or better accuracies

SUBMITTER: Hanaoka K 

PROVIDER: S-EPMC7450624 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature