Optimization of Quantum-Active Defects and Spins in Chemical Systems Using Machine Learning

Quantum materials, particularly those featuring nitrogen-vacancy (NV) centers in diamond, are essential for advancements in quantum computing, magnetometry, and high-precision sensing technologies. These materials possess unique quantum properties, such as enhanced quantum-active defects and stable spin states, which are crucial for developing reliable and efficient quantum devices. The increasing demand for high-performance quantum materials necessitates innovative manufacturing techniques that can consistently produce materials with tailored quantum characteristics, ensuring their effectiveness and scalability in various cutting-edge applications. Currently, the manufacturing of quantum materials is hindered by the complexity of the fabrication parameter space, which includes numerous variables like seed miscut angles, growth time, irradiation dose, and gas flow rates. Traditional optimization approaches struggle to manage and correlate these parameters effectively, leading to inefficient production processes and inconsistent material quality. This complexity makes it challenging to systematically enhance the quantum properties of the materials, resulting in limited scalability and reliability. Consequently, there is a pressing need for advanced methodologies that can navigate the intricate fabrication landscape to reliably produce high-quality quantum materials suited for their intended applications.

Technology Description

The technology employs machine learning models to enhance the manufacturing of quantum materials by optimizing fabrication parameters based on the characterization of their quantum properties. The process begins with creating an initial quantum material sample and evaluating its properties, such as dephasing time, contrast, and NV density in diamond systems with NV centers. Data from these characterizations are used to train regression models using advanced machine learning techniques like gradient boost, random forest, and stacking regression. These models establish correlations between fabrication parameters—including seed miscut angles, growth time, and irradiation dose—and a figure of merit that measures material quality. Using the trained models, improved fabrication parameters are identified and applied to produce subsequent samples with enhanced quantum properties through an iterative manufacturing process.

This technology stands out by integrating sophisticated machine learning algorithms to navigate the complex and high-dimensional fabrication parameter space, which traditional optimization methods find challenging. Techniques such as SHAP values for identifying impactful parameters and Bayesian optimization for systematic improvement enable precise and efficient optimization of material properties. Specifically, for diamond NV center fabrication, the approach allows for meticulous control over critical parameters, resulting in superior quantum material performance tailored for applications like magnetometry. Furthermore, the methodology is adaptable to various quantum materials beyond diamond systems, providing a scalable and versatile framework that drives significant advancements in the field of quantum material manufacturing.