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Interatomic potential is a key component of large-scale atomic simulation of materials. For scientific problems in complex environments such as high temperature，high pressure and irradiation，the interactions between atoms are often very complex. The empirical force field only considers two-body，three-body or four-body interactions between atoms. The physical assumption is simple，and it is often difficult to accurately describe the potential energy surface of complex environment. Machine learning force fields can obtain potential energy surfaces that are more accurate than empirical force fields. In this paper，a machine learning force field based on physical model is proposed for BaZrO3，the most common perovskite system，to describe the static properties，phase stability and mechanical properties of BaZrO3. The density functional theory database is used to train the machine learning force field based on physical model，and the static properties，phase stability and mechanical properties are calculated. For the static properties，the elastic constants C11，C12 and C44 were computed using both a pure machine-learning force field and a machine-learning force field based on a physical model，and the simulation results were much better than the empirical force field when compared to the DFT with errors of 0.34%，8.75% and 10.71% for the former，and 0.34%，2.5% and 7.14% for the latter. As for the phase stability，it is found that the machine learning force field based on physical model inherits the advantage of the empirical force field in maintaining the phase stability，which is better than the pure machine learning force field. For mechanical properties，Young's modulus of four different crystal directions of BaZrO3 are calculated. It was found that the errors between the calculated and experimental values for the machine learning force field and the machine learning force field based on the physical model were 9.22% and 1.6%，which were much lower than the results of the empirical force field. It can be seen that integrating physical models into the development of machine learning force field is an important way to improve the accuracy of atomic simulations.

The potential application of monolayer MS2 (M = Mo, W) as thermoelectric material has been widely studied since the first report of successful fabrication. However, their performances are hindered by the considerable band gap and the large lattice thermal conductivity in the pristine 2H phase. Recent discoveries of polymorphism in MS2s provide new opportunities for materials engineering. In this work, phonon and electron transport properties of both 2H and 1T′ phases were investigated by first-principle calculations. It is found that upon the phase transition from 2H to 1T′ in MS2, the electron transport is greatly enhanced, while the lattice thermal conductivity is reduced by several times. These features lead to a significant enhancement of power factor by one order of magnitude in MoS2 and by three times in WS2. Meanwhile, the figure of merit can reach up to 0.33 for 1T′–MoS2 and 0.68 for 1T′–WS2 at low temperature. These findings indicate that monolayer MS2 in the 1T′ phase can be promising materials for thermoelectric devices application. Meanwhile, this work demonstrates that phase engineering techniques can bring in one important control parameter in materials design.

Wide bandgap β-Ga2O3 is an ideal candidate material with broad application prospects for power electronic components in the future. Aiming at the application requirements of β-Ga2O3 in space photoelectric devices, this work studies the influence of 40 MeV Si ion irradiation on the microstructure and optical properties of β-Ga2O3 epi-wafers. Raman spectroscopy analysis confirms that Si ion irradiation destroys the symmetric stretching mode of tetrahedral–octahedral chains in β-Ga2O3 epi-wafers, and the obtained experimental evidence of irradiation leads to the enhanced defect density of VO and VGa–VO from x-ray photoelectron spectroscopy. Combined with first-principles calculations, we conclude that most configurations of VO and VGa–VO are likely non-radiative, leading to quenching of experimental photoluminescence intensity. Unraveling optical degradation mechanism and predicting the optical application of β-Ga2O3 devices in the space environment by combining ground irradiation experiments with first-principles calculations still be one of the focuses of research in the future. [ABSTRACT FROM AUTHOR]

In this work, a simple, controllable and mass-produced microcrack technique for stretchable strain sensors is presented. Specifically, the conductive layer (hydroxylated MWCNTs) and the flexible substrate (Ecoflex) of fiber strain flexible sensors are modified based on proton irradiation defect engineering. The evolution of defects in sensitive materials and the density of microcracks in flexible substrates are controlled by controlling the irradiation fluence during the irradiation process. In particular, when the irradiation fluence is 1 × 1014 p cm−2, the gauge factor (GF) of the fiber strain sensor is as high as 1274.8, and the response time and recovery time are reduced by 49.4% and 45.1%, respectively. At the same time, the microcracked structure does not fracture easily under a large strain, the maximum strain being 800%. In addition, fiber strain flexible sensors can also be used to detect biomechanical signals in different scenarios. This simple and efficient microcrack technique opens up a new prospect for the fabrication of high-performance stretchable strain sensors. [ABSTRACT FROM AUTHOR]