AI4NLO: An Integrated Data Platform for Machine Learning-Driven Exploration of Inorganic Nonlinear Optical Materials (Communications in Computational Chemistry, 2025)
Nonlinear optical (NLO) materials, with their unique wavelength conversion capabilities, play a crucial role in a widerange of scientific and industrial applications. Despite significant progress, the development of novel NLO materials, particularlythose in the deep ultraviolet and mid-infrared regions, remains a challenge. Recent advancements in machine learning (ML)technologies have injected new momentum into materials science research. In this work, we present an integrated data platformincorporating advanced ML techniques, designed to drive the discovery and exploration of inorganic NLO materials. The platformcurrently includes about 1000 entries with their structures and key properties. Users can apply built-in ML models developed inour group for immediate predictions of NLO properties or train their own models based on specific research needs. Additionally,the platform provides access to the results of deep generative models, allowing users to retrieve newly generated virtual crystalstructures, thus expanding the chemical space for NLO materials exploration. This platform not only provides reliable data supportfor researchers but also holds the potential to accelerate the discovery of novel NLO materials.
Keywords: nonlinear optical crystal; database; second harmonic generation; coefficient; birefringence; machine learning; generative artificial intelligence