Accelerating the discovery of advanced materials is crucial for modern industries, aerospace, biomedicine, and energy. Nevertheless, only a small fraction of materials are currently under experimental investigation within the vast chemical space. Materials scientists are plagued by time-consuming and labor-intensive experiments due to lacking efficient material discovery strategies. Artificial intelligence (AI) has emerged as a promising instrument to bridge this gap. Although numerous AI toolkits or platforms for material science have been developed, they suffer from many shortcomings. These include primarily focusing on material property prediction and being unfriendly to material scientists lacking programming experience, especially performing poorly with limited data. Here, we developed MLMD, an AI platform for materials design. It is capable of effectively discovering novel materials with high-potential advanced properties end-to-end, utilizing model inference, surrogate optimization, and even working in situations of data scarcity based on active learning. Additionally, it integrates data analysis, descriptor refactoring, hyper-parameters auto-optimizing, and properties prediction. It also provides a web-based friendly interface without need programming and can be used anywhere, anytime. MLMD is dedicated to the integration of material experiment/computation and design, and accelerate the new material discovery with desired one or multiple properties. It demonstrates the strong power to direct experiments on various materials (perovskites, steel, high-entropy alloy, etc). MLMD will be an essential tool for materials scientists and facilitate the advancement of materials informatics.