A Comprehensive and Versatile Multimodal Deep Learning Approach for
Predicting Diverse Properties of Advanced Materials
- URL: http://arxiv.org/abs/2303.16412v1
- Date: Wed, 29 Mar 2023 02:42:17 GMT
- Title: A Comprehensive and Versatile Multimodal Deep Learning Approach for
Predicting Diverse Properties of Advanced Materials
- Authors: Shun Muroga, Yasuaki Miki, and Kenji Hata
- Abstract summary: We present a multimodal deep learning framework for predicting physical properties of a 10-dimensional acrylic polymer composite material.
Our approach handles an 18-dimensional complexity, with 10 compositional inputs and 8 property outputs, successfully predicting 913,680 property data points across 114,210 composition conditions.
This study advances future research on different materials and the development of more sophisticated models, drawing us closer to the ultimate goal of predicting all properties of all materials.
- Score: 0.9517427900627922
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present a multimodal deep learning (MDL) framework for predicting physical
properties of a 10-dimensional acrylic polymer composite material by merging
physical attributes and chemical data. Our MDL model comprises four modules,
including three generative deep learning models for material structure
characterization and a fourth model for property prediction. Our approach
handles an 18-dimensional complexity, with 10 compositional inputs and 8
property outputs, successfully predicting 913,680 property data points across
114,210 composition conditions. This level of complexity is unprecedented in
computational materials science, particularly for materials with undefined
structures. We propose a framework to analyze the high-dimensional information
space for inverse material design, demonstrating flexibility and adaptability
to various materials and scales, provided sufficient data is available. This
study advances future research on different materials and the development of
more sophisticated models, drawing us closer to the ultimate goal of predicting
all properties of all materials.
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