Multimodal Intelligence in Chemical Discovery: Integrating Interpretable ML, Autonomous Robotics, and Edge Computing

The fusion of machine learning is catalyzing a paradigm shift in chemistry research from empirical exploration to data-driven discovery. This review systematically summarizes the cutting-edge progress of machine learning algorithms in breaking through the limitations of traditional chemical research. In terms of reverse design, diffusion models have achieved a structural reconstruction accuracy of up to 93.4%, and closed-loop verification has been achieved through Rietveld refinement. Regarding the interpretable multimodal intelligence, by combining Shapley additive explanations (SHAP) analysis and physical constraint architecture, the structure-activity relationship across spectral, microscopic, and time series data has been successfully decoded through effective fusion of multimodal data and algorithms. For the embedded machine learning systems, the deployment of lightweight convolutional neural networks (CNN) and edge computing platforms provides real-time control capabilities for industrial synthesis and environmental monitoring via tight integration of algorithmic and system modalities. More importantly, we have revealed the existing challenges, including the generalization gap in low-symmetry systems, limitations in dynamic process modeling, and data heterogeneity in cross-modality integration. This study has drawn a development blueprint for the next generation of chemical intelligent systems, which integrates physical perception algorithms with automated experiments to ultimately achieve programmable material design.