AlphaMat: a material informatics hub connecting data, features, models and applications

ZL Wang and A Chen and KH Tao and JF Cai and YQ Han and J Gao and SM Ye and SW Wang and I Ali and JJ Li, NPJ COMPUTATIONAL MATERIALS, 9, 130 (2023).

DOI: 10.1038/s41524-023-01086-5

The development of modern civil industry, energy and information technology is inseparable from the rapid explorations of new materials. However, only a small fraction of materials being experimentally/computationally studied in a vast chemical space. Artificial intelligence (AI) is promising to address this gap, but faces many challenges, such as data scarcity and inaccurate material descriptors. Here, we develop an AI platform, AlphaMat, that can complete data preprocessing and downstream AI models. With high efficiency and accuracy, AlphaMat exhibits strong powers to model typical 12 material attributes (formation energy, band gap, ionic conductivity, magnetism, bulk modulus, etc.). AlphaMat's capabilities are further demonstrated to discover thousands of new materials for use in specific domains. AlphaMat does not require users to have strong programming experience, and its effective use will facilitate the development of materials informatics, which is of great significance for the implementation of AI for Science (AI4S).

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