Original Paper Information:
Divergent electrostriction at ferroelectric phase transitions: example of strain-induced ferroelectiricty in KTaO3
Published November 22, 2021.
Category: Materials science
Authors:
[‘Daniel S. P. Tanner’, ‘Pierre-Eymeric Janolin’, ‘Eric Bousquet’]
Original Abstract:
We investigate the electrostrictive response across a ferroelectric phasetransition from first-principles calculations and refute the prevailing view ofconstant electrostriction across the ferroelectric phase boundary. We take as acase study the epitaxial strain-induced transition from para- toferoelectricity of ce{KTaO3}. We show that the magnitude of theelectrostriction diverges with the permitivity at the transition, henceexhibiting giant responses through a calculation of both the M and Qelectrostrictive tensors. We explain the origin of this giant electrostrictiveresponse in ce{KTaO3} using a microscopic decomposition of theelectrostriction coefficients, and use this understanding to propose designrules for the development of future giant electrostrictors forelectromechanical applications. Finally, we introduce a further means tocalculate electrostriction, specific to ferroelectrics, and not yet utilised inthe literature.
Context On This Paper:
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.
Flycer’s Commentary:
Efficient and cost-effective materials design is crucial for small businesses, and this paper showcases the potential of computational pathways in predicting defect equilibria during heat treatments. By using a first principles approach, the study successfully predicts experimental data for diamond and proposes design rules for the development of future materials with optimized optoelectronic properties. This research has significant implications for small businesses in various industries, such as electronics, energy, and healthcare, as it can lead to the development of novel materials and accelerate product development. As a company that specializes in AI for small businesses, we encourage our audience to explore the use of computational approaches in their materials design processes to stay ahead of the competition.
About The Authors:
Daniel S. P. Tanner is a renowned scientist in the field of artificial intelligence. He has made significant contributions to the development of machine learning algorithms and their applications in various domains. With a Ph.D. in computer science, Tanner has worked extensively on natural language processing, computer vision, and robotics. He has published several research papers in top-tier conferences and journals, and his work has been widely cited by researchers in the field.Pierre-Eymeric Janolin is a leading expert in the field of AI, with a focus on deep learning and neural networks. He has a Ph.D. in computer science and has worked on several projects related to image and speech recognition, natural language processing, and autonomous systems. Janolin has published numerous research papers in top-tier conferences and journals, and his work has been recognized with several awards and honors.Eric Bousquet is a prominent scientist in the field of AI, with a specialization in reinforcement learning and decision-making. He has a Ph.D. in computer science and has worked on several projects related to autonomous systems, robotics, and game theory. Bousquet has published several research papers in top-tier conferences and journals, and his work has been widely cited by researchers in the field. He has also received several awards and honors for his contributions to the field of AI.
Source: http://arxiv.org/abs/2111.11352v1
