shi et al 2024 phonon stability boundary and deep elastic strain engineering of lattice thermal conductivity.jpg 768x768 q85 subsampling 2 TACC Supercomputers Enable Tuning of Diamond's Thermal Conductivity

TACC Supercomputers Enable Tuning of Diamond’s Thermal Conductivity

Researchers have made a groundbreaking discovery about the thermal conductivity of diamond, the hardest material found in nature. An international team of scientists found that by flexing diamond, its thermal conductivity can be drastically tuned up or down. This finding opens up new possibilities for developing microelectronic and optoelectronic devices such as computer chips, quantum sensors, and communication devices.

The study, led by former MIT researcher Frank Shi, was published in the Proceedings of the National Academy of Sciences in February 2024. The team used supercomputer simulations to explore the six-dimensional strain space of diamond, finding that its thermal conductivity can be increased or decreased by more than 90% through mechanical strains without inducing instabilities.

To achieve these results, the scientists utilized the National Science Foundation-funded Frontera supercomputer at the Texas Advanced Computing Center (TACC). By generating data from the large strain space and the three-dimensional phonon band structure, they were able to map out the elastic strain space governing diamond’s phonon stability and thermal conductivity.

Through thousands of density functional theory calculations and machine learning, the team was able to delineate the six-dimensional ‘ideal strain’ surface of diamond. This work has significant implications for the development of advanced materials used in various applications like batteries and computer chips.

The researchers emphasize the importance of supercomputers in accelerating the materials science discovery process. By enabling simulations and iterative modeling based on new data, supercomputers like Frontera play a crucial role in advancing material design and practical applications. This study represents a significant step forward in the field of materials science and highlights the power of combining computational tools with experimental research.

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