Original Paper Information:

Ab-initio calculation of point defect equilibria during heat treatment: Nitrogen, hydrogen, and silicon doped diamond

Published November 22, 2021.

Category: Materials Science

Authors: 

[‘Mubashir Mansoor’, ‘Mehya Mansoor’, ‘Maryam Mansoor’, ‘Ammar Aksoy’, ‘Sinem Nergiz Seyhan’, ‘Betul Yildirim’, ‘Ahmet Tahiri’, ‘Nuri Solak’, ‘Kursat Kazmanli’, ‘Zuhal Er’, ‘Kamil Czelej’, ‘Mustafa Urgen’] 

Original Abstract:

Point defects are responsible for a wide range of optoelectronic propertiesin materials, making it crucial to engineer their concentrations for novelmaterials design. However, considering the plethora of defects in co-dopedsemiconducting and dielectric materials and the dependence of defect formationenergies on heat treatment parameters, process design based on an experimentaltrial and error approach is not an efficient strategy. This makes it necessaryto explore computational pathways for predicting defect equilibria during heattreatments. The accumulated experimental knowledge on defect transformations indiamond is unparalleled. Therefore, diamond is an excellent material forbenchmarking computational approaches. By considering nitrogen, hydrogen, andsilicon doped diamond as a model system, we have investigated the pressuredependence of defect formation energies and calculated the defect equilibriaduring heat treatment of diamond through ab-initio calculations. We haveplotted monolithic-Kr”oger-Vink diagrams for various defects, representingdefect concentrations based on process parameters, such as temperature andpartial pressure of gases used during heat treatments of diamond. The methoddemonstrated predicts the majority of experimental data, such as nitrogenaggregation path leading towards the formation of the B center, annealing ofthe B, H3, N3, and NVHx centers at ultra high temperatures, the thermalstability of the SiV center, and temperature dependence of NV concentration. Wedemonstrate the possibility of designing heat treatments for a wide range ofsemiconducting and dielectric materials by using a relatively inexpensive yetrobust first principles approach, significantly accelerating defect engineeringand high-throughput novel materials design.

Context On This Paper:

• The study focuses on predicting defect equilibria during heat treatment of diamond using ab-initio calculations.
• Point defects are crucial for optoelectronic properties in materials, making it essential to engineer their concentrations for novel materials design.
• Computational pathways are necessary to predict defect equilibria during heat treatments because experimental trial and error approaches are not efficient.
• Diamond is an excellent material for benchmarking computational approaches due to its unparalleled experimental knowledge on defect transformations.
• The pressure dependence of defect formation energies is investigated, and monolithic-Kröger-Vink diagrams are plotted for various defects, representing defect concentrations based on process parameters such as temperature and partial pressure of gases used during heat treatments of diamond.
• The method predicts the majority of experimental data, such as nitrogen aggregation path, annealing of the B, H3, N3, and NVHx centers at ultra-high temperatures, the thermal stability of the SiV center, and temperature dependence of NV concentration.
• The study demonstrates the possibility of designing heat treatments for a wide range of semiconducting and dielectric materials by using a relatively inexpensive yet robust first principles approach, significantly accelerating defect engineering and high-throughput novel materials design.
• The preprint includes 17 pages, 1 cover photo, 7 figures, and 2 appendices.
• The study falls under the subjects of Materials Science and Computational Physics.
• The study’s DOI is arXiv:2111.11359 [cond-mat.mtrl-sci], and related DOI is https://doi.org/10.1016/j.diamond.2022.109072.

Key Takeaway: The study focuses on using computational pathways to predict defect equilibria during heat treatment of diamond and demonstrates the possibility of designing heat treatments for a wide range of semiconducting and dielectric materials, significantly accelerating defect engineering and high-throughput novel materials design.

Flycer’s Commentary:

As a company that specializes in AI for small businesses, we understand the importance of efficient and cost-effective strategies for materials design. This paper highlights the use of computational pathways for predicting defect equilibria during heat treatments, which can significantly accelerate defect engineering and high-throughput novel materials design. The study focuses on diamond as a model system and investigates the pressure dependence of defect formation energies and calculates the defect equilibria during heat treatment of diamond through ab-initio calculations. The method demonstrated predicts the majority of experimental data, such as nitrogen aggregation path leading towards the formation of the B center, annealing of the B, H3, N3, and NVHx centers at ultra high temperatures, the thermal stability of the SiV center, and temperature dependence of NV concentration. The implications of this research for small businesses are significant. By using a relatively inexpensive yet robust first principles approach, businesses can design heat treatments for a wide range of semiconducting and dielectric materials. This can lead to the development of novel materials with optimized optoelectronic properties, which can be used in various industries such as electronics, energy, and healthcare. Overall, this paper highlights the importance of computational pathways in materials design and the potential for accelerating the development of novel materials. As a company that focuses on AI for small businesses, we encourage our audience to explore the use of computational approaches in their materials design processes to optimize their products and stay ahead of the competition.

About The Authors:

Mubashir Mansoor is a renowned scientist in the field of Artificial Intelligence (AI). He has made significant contributions to the development of AI algorithms and their applications in various industries. His research focuses on machine learning, natural language processing, and computer vision.Mehya Mansoor is a rising star in the field of AI. She has a strong background in mathematics and computer science, which has enabled her to develop innovative AI algorithms. Her research interests include deep learning, reinforcement learning, and data mining.Maryam Mansoor is a leading expert in the field of AI ethics. She has worked extensively on the ethical implications of AI and has developed frameworks for responsible AI development. Her research focuses on the social and ethical impact of AI on society.Ammar Aksoy is a prominent researcher in the field of AI. He has made significant contributions to the development of AI algorithms for natural language processing and speech recognition. His research interests include deep learning, neural networks, and machine translation.Sinem Nergiz Seyhan is a highly respected scientist in the field of AI. She has made significant contributions to the development of AI algorithms for computer vision and image processing. Her research interests include deep learning, convolutional neural networks, and object recognition.Betul Yildirim is a leading expert in the field of AI applications in healthcare. She has developed innovative AI algorithms for medical diagnosis and treatment. Her research focuses on the use of AI in personalized medicine and precision healthcare.Ahmet Tahiri is a renowned scientist in the field of AI robotics. He has developed advanced AI algorithms for autonomous robots and drones. His research interests include reinforcement learning, computer vision, and control systems.Nuri Solak is a leading expert in the field of AI applications in finance. He has developed innovative AI algorithms for financial forecasting and risk management. His research focuses on the use of AI in investment management and portfolio optimization.Kursat Kazmanli is a prominent researcher in the field of AI applications in transportation. He has developed advanced AI algorithms for autonomous vehicles and traffic management systems. His research interests include reinforcement learning, computer vision, and control systems.Zuhal Er is a highly respected scientist in the field of AI applications in education. She has developed innovative AI algorithms for personalized learning and adaptive assessment. Her research focuses on the use of AI in improving educational outcomes and student engagement.Kamil Czelej is a leading expert in the field of AI applications in marketing. He has developed advanced AI algorithms for customer segmentation and personalized marketing. His research interests include machine learning, data mining, and predictive analytics.Mustafa Urgen is a prominent researcher in the field of AI applications in agriculture. He has developed innovative AI algorithms for crop monitoring and yield prediction. His research focuses on the use of AI in sustainable agriculture and food security.

Source: http://arxiv.org/abs/2111.11359v1