Search

Atomistic simulations hold significant value in clarifying crucial phenomena such as phase transitions and energy transport in materials science. Their success stems from the presence of potential energy functions capable of accurately depicting the relationship between system energy and lattice changes. In magnetic materials, two atomic scale degrees of freedom come into play: the lattice and the spin. However, accurately tracing the simultaneous evolution of both lattice and spin in magnetic materials at an atomic scale is a substantial challenge. This is largely due to the complexity involved in depicting the interaction energy precisely, and its influence on lattice and spin-driving forces, such as atomic force and magnetic torque, which continues to be a daunting task in computational science. Addressing this deficit, we present DeepSPIN, a versatile approach that generates high-precision predictive models of energy, atomic forces, and magnetic torque in magnetic systems. This is achieved by integrating first-principles calculations of magnetic excited states with deep learning techniques via active learning. We thoroughly explore the methodology, accuracy, and scalability of our proposed model in this paper. Our technique adeptly connects first-principles computations and atomic-scale simulations of magnetic materials. This synergy presents opportunities to utilize these calculations in devising and tackling theoretical and practical obstacles concerning magnetic materials.

Atomic simulations hold significant value in clarifying crucial matters such as phase transitions and energy transport in materials science. Their success stems from the presence of potential energy functions capable of accurately depicting the relationship between system energy and lattice changes. In magnetic materials, two atomic scale degrees of freedom come into play: the lattice and the magnetic moment. Nonetheless, precisely portraying the interaction energy and its impact on lattice and spin-driving forces, such as atomic force and magnetic torque, remains a formidable task in the computational domain. Consequently, there is no atomic-scale approach capable of elucidating the evolution of lattice and spin at the same time in magnetic materials. Addressing this knowledge deficit, we present DeepSPIN, a versatile approach that generates high-precision predictive models of energy, atomic forces, and magnetic torque in magnetic systems. This is achieved by integrating first-principles calculations of magnetic excited states with advanced deep learning techniques via active learning. We thoroughly explore the methodology, accuracy, and scalability of our proposed model in this paper. Our technique adeptly connects first-principles computations and atomic-scale simulations of magnetic materials. This synergy presents opportunities to utilize these calculations in devising and tackling theoretical and practical obstacles concerning magnetic materials., 8 pages, 4 figures

A superhydrophobic copper foam (CF) with excellent robustness and durability was successfully fabricated by using an in situ growth method combined with surface hexadecanethiol (HDT) modification of HKUST-1/CF. The wettability mechanism for the conversion of the superhydrophilic to superhydrophobic states was clarified and explored via density functional theory (DFT). The as-prepared HKUST-1/HDT/CF surface possessed good superhydrophobicity under the optimal conditions including the mass ratio of copper nitrate trihydrate (Cu(NO3)2·3H2O) to 1,3,5-benzenetricarboxylicacid (H3BTC), HDT immersion time, in situ growth time and hydrothermal reaction temperature. The superhydrophobic surface can maintain a desirable hydrophobic state after immersing in different pH solutions, abrasion of weight, and continuous ultrasonic testing. The highest water contact angle (WCA) value of the obtained samples was 162.9° under the aforementioned harsh conditions. Additionally, the superhydrophobic CF has a bright future in the field of oil/water separation due to the obtained HKUST-1/HDT/CF surface with excellent water-repellent performance.

A sufficient understanding of mechanical performance of self-anchored suspension bridge with double-sided steel box girder is essential for design and normal use as such bridges are widely built in urban bridge. Using the Yunlongwan Bridge which is a suspension bridge with ultra-wide double-sided steel box girder as an example, this paper investigates its deformation and mechanical performance under vehicle load. Firstly, based on the field test results, the deformation performance of the bridge and the stress distribution of the main girder are analysed, with emphasis on the shear lag effect of double-sided steel box girder. Then, a multiscale model of the bridge was built, and the accuracy of the model was verified by comparison with the test data. Finally, the influence of design parameters on the mechanical behaviour of double-sided steel box girder is studied by numerical simulation. The results show that the deformation of the bridge has good symmetry, there is obvious shear lag effect on the main girder, and the U-rib thickness, diaphragm spacing, and vehicle load could significantly affect the stress of the main girder top plate. The obtained analytical results lead to a better understanding of the mechanical performance and provide reference for the design of self-anchored suspension bridge with double-sided steel box girder.

Earthquakes can cause serious damage to traffic infrastructures, among which the impact on bridge structure is the most important. Therefore, in order to assess bridges serviceability, it is important to master their damage mechanism and to analyze its probability of occurrence under a given seismic action. Various uncertainties, like the location of epicentre of future earthquakes and their magnitudes, make this task quite challenging. We are also required to consider different earthquake scenarios and the damaged states of bridge components associated with those earthquakes. To suppress these difficulties, this study proposed a new method of performance-based seismic fragility and risk assessment for bridges. The proposed method included three steps: (1) performance-based seismic fragility estimation of a five-span continuous rigid frame bridge, (2) seismic hazard analysis for locations of the bridge, and (3) seismic risk analysis of the bridge. The proposed method that considered the performance of the bridge and the uncertainty in the location of the earthquake epicentre and magnitudes can provide valuable references for seismic-resistant design of multispan continuous rigid frame bridges in the future.