Machine Learning in Semiconductor Science

The energy band gap is a crucial parameter in determining the functionalities of a semiconductor in optics, optoelectronics, microelectronics, and photovoltaics applications [1,2]. Consequently, considerable effort has been made to develop theoretical methods for high-throughput calculations of this electronic property. Over the past decades, ab initio simulations have been widely used to calculate the electronic properties of a large number of materials, leading to the creation of large-scale materials data and computational infrastructures [3–8]. These ever-growing data infrastructures provide fertile grounds for the emergence of novel materials modeling by employing data-driven approaches [9–11]. Machine learning (ML) [12] and artificial intelligence [13] have become increasingly popular as efficient machinery for predicting material properties. The most pertinent aspect of every ML model for materials property prediction is properly encoding material information, including chemical composition and atomistic structure [14]. The role of these materials’ representations is to capture the underlying mechanism of a desired materials property and map materials samples to feature spaces amenable to learning approaches. We aim to evaluate the performance and identify the domain of applicability of ML for predicting the band gap of semiconductors and insulators using different classes of structural features. The special significance of our research is its potential to produce a transferable ML model for reliable prediction of the theoretical band gap of semiconductors and insulators. In other words, this model is likely not limited to specific datasets or certain structures. In this regard, we will quest for three central requirements: the best combinations of primary features, the most efficient structural features, and the broadest domain of applicability for ML prediction of the theoretical band gap of materials.

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