Materials Informatics closely integrates materials science, computer science, artificial intelligence and other disciplines. It is an important way to accelerate the development of materials science to establish a materials informatics platform to support commonly used materials data preprocessing functions and artificial intelligence algorithms, and does not require strong computer programming skills to improve the efficiency of AI modeling for more scientists. In this study, a materials informatics platform with more than 90 commonly used functions (data collection data preprocessing feature engineering model establishment parameter optimization model evaluation result analysis) was developed. Prof. Jinjin Li and co-authors (Artificial Intelligence and Micro-structure Laboratory, Shanghai Jiao Tong University) developed AlphaMat, an all-in-one platform for materials informatics, by integrating materials computing tools and artificial intelligence modeling methods. Alphamat has been applied in 12 commonly used material property modeling. AlphaMat saves significant time cost and hardware cost in material discovery, and the authors used AlphaMat to successfully identify 491 potential photovoltaic materials, 78 metallic electrode materials, 9 solid-state electrolytes, 58 thermal-conductivity materials, and 39 Li-S battery cathodes. With AlphaMat, users can easily build AI models at any data scale to discover and design materials. Following the principles of interactivity, scalability, efficiency, and intelligence, AlphaMat, along with many other toolkits built by the larger materials community, promises to facilitate and accelerate advances in materials science, computer science, and physical and chemical sciences. AlphaMat can deeply integrate materials science and artificial intelligence methods, and is expected to become an essential tool for materials science research, which is of great significance for promoting the development of AI for Science.