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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. 

In applications involving fretting wear damage, surfaces with high yield strength and wear resistance are required. In this study, the mechanical responses of materials with graded nanostructured surfaces during fretting sliding are investigated and compared to homogeneous materials through a systematic computational study. A three-dimensional finite element model is developed to characterize the fretting sliding characteristics and shakedown behavior with varying degrees of contact friction and gradient layer thicknesses. Results obtained using a representative model material (i.e., 304 stainless steel) demonstrate that metallic materials with a graded nanostructured surface could exhibit a more than 80% reduction in plastically deformed surface areas and volumes, resulting in superior fretting damage resistance in comparison to homogeneous coarse-grained metals. In particular, a graded nanostructured material can exhibit elastic or plastic shakedown, depending on the contact friction coefficient. Optimal fretting resistance can be achieved for the graded nanostructured material by decreasing the friction coefficient (e.g., from 0.6 to 0.4 in 304 stainless steel), resulting in an elastic shakedown behavior, where the plastically deformed volume and area exhibit zero increment in the accumulated plastic strain during further sliding. These findings in the graded nanostructured materials using 304 stainless steel as a model system can be further tailored for engineering optimal fretting damage resistance. 

Physics-informed deep learning helps detect unknown internal structures and defects with limited nondestructive measurements.