CEEM: a Chemically Explainable Deep Learning Platform for Identifying Compounds with Low Effective Mass

Abstract The semiconductor industry occupies a crucial position in the fields of integrated circuits, energy, and communication systems. Effective mass ( m E ), which is closely related to electron transition, thermal excitation, and carrier mobility, is a key performance indicator of semiconductor. However, the highly neglected m E is onerous to measure experimentally, which seriously hinders the evaluation of semiconductor properties and the understanding of the carrier migration mechanisms. Here, a chemically explainable effective mass predictive platform (CEEM) is constructed by deep learning, to identify n‐type and p‐type semiconductors with low m E . Based on the graph network, a versatile explainable network is innovatively designed that enables CEEM to efficiently predict the m E of any structure, with the area under the curve of 0.904 for n‐type semiconductors and 0.896 for p‐type semiconductors, and derive the most relevant chemical factors. Using CEEM, the currently largest m E database is built that contains 126 335 entries and screens out 466 semiconductors with low m E for transparent conductive materials, photovoltaic materials, and water‐splitting materials. Moreover, a user‐friendly and interactive CEEM web is provided that supports query, prediction, and explanation of m E . CEEM's high efficiency, accuracy, flexibility, and explainability open up new avenues for the discovery and design of high‐performance semiconductors.