Multimodal human-in-the-loop artificial intelligence with affective feedback for accelerated high-entropy alloy discovery

simulations, which struggle with high computational costs and limited predictive accuracy. Existing machine learning approaches, while promising, remain constrained by data scarcity, limited interpretability, and the lack of effective human-AI interaction. To address these limitations, we introduce an integrated human-computer interactive HEA design platform that incorporates emotional feedback. By combining natural language processing (T5 model), machine learning (XGBoost), and multi-objective optimization (NSGA-II), the platform establishes a closed-loop "perception-decision-optimization" workflow. Real-time emotion recognition dynamically adjusts optimization weights, enabling efficient human-AI collaboration. The model achieves high accuracy in predicting yield strength and Young's modulus, with SHAP analysis revealing the underlying physical mechanisms. Emotion-driven optimization guides Pareto front convergence, with results showing