Data‐Driven Review and Machine Learning Prediction of Diamond Vacancy Center Synthesis

Diamond and diamond color centers have become prime hardware candidates for solid‐state‐based technologies in quantum information and computing, optics, photonics, and (bio)sensing. The synthesis of diamond materials with specific characteristics and the precise control of the hosted color centers is thus essential to meet the demands of advanced applications. Yet, challenges remain in improving the concentration, uniform distribution, and quality of these centers. Here, we perform a review and meta‐analysis of some of the main diamond synthesis methods and their parameters for the synthesis of N‐, Si‐, Ge‐, and Sn‐vacancy color centers. We extract quantitative data from over 60 experimental articles and organize it in a large database (170 datasets and 1692 entries). We then use the database to train two machine learning algorithms to make robust predictions about the fabrication of diamond materials with specific properties from careful combinations of synthesis parameters. We use traditional statistical indicators to benchmark the performance of the algorithms and show that they are powerful and resource‐efficient tools for researchers and material scientists working with diamond color centers and their applications.