Multimodal neural network-based predictive modeling of nanoparticle properties from pure compounds

Simulating complex and large materials is a challenging task that requires extensive domain knowledge and computational expertise. This study introduces Pure2DopeNet, an innovative multimodal neural network that tackles these challenges by integrating image and text data to accurately predict the physical properties of doped compounds, specifically Carbon (C)-doped TiO2 and Sulfur (S)-doped ZnO nanoparticles. The model achieves quantum mechanical level accuracy, comparable to density functional tight binding (DFTB), across various doping levels, demonstrating its capability to determine the properties from a single simulation of the pure compound. Pure2DopeNet outperforms traditional deep learning architectures such as ResNet, ViT, and CoAtNet, delivering superior accuracy, faster performance, and reduced dependence on domain expertise. This approach highlights the potential of multimodal machine learning to revolutionize materials science by making high-fidelity simulations more accessible and efficient, opening paving the way for material discovery and the exploration of novel properties.