Phonon stability boundary and deep elastic strain engineering of lattice thermal conductivity.
Zhe ShiEvgenii TsymbalovWencong ShiAriel BarrQingjie LiJiangxu LiXing-Qiu ChenMing DaoSubra SureshJu LiPublished in: Proceedings of the National Academy of Sciences of the United States of America (2024)
Recent studies have reported the experimental discovery that nanoscale specimens of even a natural material, such as diamond, can be deformed elastically to as much as 10% tensile elastic strain at room temperature without the onset of permanent damage or fracture. Computational work combining ab initio calculations and machine learning (ML) algorithms has further demonstrated that the bandgap of diamond can be altered significantly purely by reversible elastic straining. These findings open up unprecedented possibilities for designing materials and devices with extreme physical properties and performance characteristics for a variety of technological applications. However, a general scientific framework to guide the design of engineering materials through such elastic strain engineering (ESE) has not yet been developed. By combining first-principles calculations with ML, we present here a general approach to map out the entire phonon stability boundary in six-dimensional strain space, which can guide the ESE of a material without phase transitions. We focus on ESE of vibrational properties, including harmonic phonon dispersions, nonlinear phonon scattering, and thermal conductivity. While the framework presented here can be applied to any material, we show as an example demonstration that the room-temperature lattice thermal conductivity of diamond can be increased by more than 100% or reduced by more than 95% purely by ESE, without triggering phonon instabilities. Such a framework opens the door for tailoring of thermal-barrier, thermoelectric, and electro-optical properties of materials and devices through the purposeful design of homogeneous or inhomogeneous strains.
Keyphrases
- room temperature
- machine learning
- density functional theory
- ionic liquid
- molecular dynamics simulations
- molecular dynamics
- physical activity
- deep learning
- escherichia coli
- small molecule
- artificial intelligence
- high throughput
- monte carlo
- high resolution
- atomic force microscopy
- quantum dots
- single molecule
- high speed
- ultrasound guided