A Comprehensive and Versatile Multimodal Deep Learning Approach for Predicting Diverse Properties of Advanced Materials

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.

arXiv comment38 pages, 17 figures, 1 table

References in corpus (8)

• High-throughput electronic band structure calculations: challenges and tools
• Pseudopotentials for high-throughput DFT calculations
• A Generative Model for Inverse Design of Metamaterials
• Towards Universal Neural Network Potential for Material Discovery Applicable to Arbitrary Combination of 45 Elements
• Predicting Crystal Structures with Data Mining of Quantum Calculations
• RadonPy: Automated Physical Property Calculation using All-atom Classical Molecular Dynamics Simulations for Polymer Informatics
• Mastering processing-microstructure complexity through the prediction of thin film structure zone diagrams by generative machine learning models
• A Comprehensive and Versatile Multimodal Deep Learning Approach for Predicting Diverse Properties of Advanced Materials

Cited by in corpus (3)

• A Comprehensive and Versatile Multimodal Deep Learning Approach for Predicting Diverse Properties of Advanced Materials
• Materials Informatics: Emergence To Autonomous Discovery In The Age Of AI
• Revealing the Evolution of Order in Materials Microstructures Using Multi-Modal Computer Vision