Data-driven studies of two-dimensional materials and their nonlinear optical properties

Our research uses data-driven methods to investigate nonlinear optical (NLO) properties in van der Waals (vdW) materials. Utilizing density functional theory calculations and machine learning (ML), we aim to expedite the discovery of stable vdW materials while predicting their crucial second-order susceptibility for NLO phenomena.

We begin by harnessing the Computational 2D Materials Database (C2DB), which comprises 268 non-centrosymmetric, non-magnetic semiconductor monolayers and their corresponding second-order susceptibility tensors across various energy ranges. We data mine C2DB's second-harmonic generation (SHG) spectra to create our dataset.

We use a random forest classifier to determine whether a material exhibits NLO properties. Subsequently, a random forest regression model predicts the second-order susceptibility for three energy ranges (0.4-0.9eV, 0.9-1.6eV, and 1.6-2.1eV). The choice of materials descriptors, derived from atomic properties, influences model performance.

Our research uses ML to shed light on the microscopic origins of NLO phenomena in vdW materials. This study paves the way for the rapid discovery of chemically stable vdW materials capable of second-harmonic generation, with applications spanning photonics and optoelectronics.