Deep Neural Networks for Multicomponent Molecular Systems.

Kyohei Hanaoka

doi:10.1021/acsomega.0c02599

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 compared to a well-used molecular descriptor-based model in the best model for each case study. Furthermore, the case studies also showed that, for ML tasks where the molecular structure information plays a minor role, the performance improvements by DNN models were small; while for ML tasks where the molecular structure information plays a major role, the performance improvements by DNN models were large, and DNN models showed notable predictive accuracies for an extremely sparse dataset, which cannot be modeled without the molecular structure information. The enhanced predictive ability of DNN models for sparse datasets of multicomponent systems will extend the applicability of ML in the multicomponent material design. Furthermore, the general capability of MEIA to extend DNN models to multicomponent systems will provide new opportunities to utilize the progress of actively developed single-component DNNs for the modeling of multicomponent systems.

Highlights

Multicomponent molecular systems such as polymer alloys, mixtures, and composite materials are used in various applications due to multiple functions and tunable properties
Most of molecular machine learning studies have focused on single-component systems,[5] attempts have been made to model properties of multicomponent molecular systems using datasets generated from massive computer simulations[6] or systematic experiments.[7−23] it is rarely possible to obtain sufficient datasets for multicomponent materials due to the huge chemical space involved, and handling of a machine learning task on sparse datasets, which are produced on a trial-and-error basis during the process of the material design, is needed to extend the applicability of machine learning-based approaches for the multicomponent materials design
Using the MEIA architecture, both graph convolution (GC) and message passing neural network (MPNN) models were successfully extended to multicomponent systems by the same procedure, and the best one in MEIA-based models consistently showed comparative or higher accuracies relative to the descriptor-based baseline in all four case studies

Summary

Introduction

Multicomponent molecular systems such as polymer alloys, mixtures, and composite materials are used in various applications due to multiple functions and tunable properties. Most of molecular machine learning studies have focused on single-component systems,[5] attempts have been made to model properties of multicomponent molecular systems using datasets generated from massive computer simulations[6] or systematic experiments.[7−23] it is rarely possible to obtain sufficient datasets for multicomponent materials due to the huge chemical space involved, and handling of a machine learning task on sparse datasets, which are produced on a trial-and-error basis during the process of the material design, is needed to extend the applicability of machine learning-based approaches for the multicomponent materials design. Such a machine learning task is still challenging for current molecular machine learning models

Methods

Results

Conclusion