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.

翻译：我们提出了一种多模态深度学习（MDL）框架，通过融合物理属性与化学数据，预测一种十维丙烯酸聚合物复合材料的物理性质。该MDL模型包含四个模块，其中三个为用于材料结构表征的生成式深度学习模型，第四个为性质预测模型。我们的方法能处理18维复杂度（包含10个组分输入和8个性质输出），成功预测了114,210种组分条件下的913,680个性质数据点。这种复杂度在计算材料科学领域前所未有，尤其针对结构未定义的材质。我们提出了一个用于逆向材料设计的高维信息空间分析框架，在数据充足的条件下，可灵活适应各类材料与尺度。本研究推动了不同材料领域的后续研究及更复杂模型的发展，使我们更接近"预测所有材料的所有性质"这一终极目标。