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.

通过融合物理属性和化学数据，我们提出了一个多模态深度学习框架，用于预测一种 10 维丙烯酸聚合物复合材料的物理特性。该框架可以处理 18 维的复杂性，成功预测了 114,210 种成分条件下的 913,680 个物性数据点。我们提出了一个分析高维信息空间以进行反向材料设计的框架，并展示了足够的数据可实现的材料类型和规模的灵活性和适应性。这项研究推动了未来对不同材料和更复杂模型的研究，并将我们更接近预测所有材料的所有属性的终极目标。