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

TL;DR

This paper reviews diamond synthesis methods, compiles a large database of experimental data, and employs machine learning to predict optimal synthesis parameters for desired color centers, aiding advanced quantum and sensing applications.

Contribution

It creates a comprehensive database from over 60 studies and applies machine learning to predict synthesis outcomes, a novel approach in diamond material fabrication research.

Findings

Machine learning algorithms accurately predict synthesis parameters.

The database contains 170 data sets and 1692 entries.

Predictions are resource-efficient and useful for researchers.

Abstract

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 papers and organize it in a large database (170 data sets 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.