First-Principles Prediction of Substrate Induced Changes in Layered Nanomaterials via Physics-Based Machine Learning

Advances in nanofabrication techniques offer remarkable control over epitaxial growth of atomically thin 2-dimensional (2D) semiconductors. However, the growing substrates could strongly influence their optical, electrical, mechanical, and chemical properties. First-principles studies revealed that substrate induced strain, in particular, can tune the electronic transport properties of layered materials, offering routes to modulate properties by strain engineering. It remains a challenge to model electronic properties of layered nanomaterials scanning the vast fabrication dependent parameter space. It is highly desirable to formulate a method that is capable to learn the information from high-cost calculations and predict propertie of a wide range of configurations. We use a machine learning model to capture the relationships between local atomic environments and electronic properties of small test systems and apply the model to predict the interface states and band dispersions of 2D semiconductors grown on different substrates. Our prediction is validated against measurements obtained from high precision techniques such as angle-resolved photoemission spectroscopy.