arXiv subjects

Christine Wu

Publications and source records attributed to Christine Wu.

3 recordsLinked to original sources

arXiv2026

Efficient method for calculation of low-temperature phase boundaries

Understanding phase stability and phase transformations is central to predicting material behavior under varying thermodynamic conditions. One of the earliest and most influential applications of density functional theory in materials science has been the prediction of pressure-induced phase transitions at 0 K. Extending these calculations to finite temperatures, however, requires accounting for thermal, quantum, and anharmonic contributions to the free energy, often at significant computational cost. In this work, we present a general and efficient framework for calculating low-temperature phase boundaries by combining the Clausius-Clapeyron equation with the quasi-harmonic approximation. This methodology requires a minimal number of calculations, while naturally incorporating internal degrees of freedom as well as quantum and low-order anharmonic effects. We illustrate the accuracy and efficiency of the approach by constructing the phase diagram of silica in the pressure range from -2 to 12 GPa and temperatures up to 1750 K. To this end, we employ a machine-learned interatomic potential trained on density functional theory reference data, enabling well-converged free energy estimates via efficient thermodynamic sampling and a rigorous comparison between the proposed framework and free energy integration.

cond-mat.mtrl-sci↗

arXiv2024

MonoTAKD: Teaching Assistant Knowledge Distillation for Monocular 3D Object Detection

Monocular 3D object detection (Mono3D) holds noteworthy promise for autonomous driving applications owing to the cost-effectiveness and rich visual context of monocular camera sensors. However, depth ambiguity poses a significant challenge, as it requires extracting precise 3D scene geometry from a single image, resulting in suboptimal performance when transferring knowledge from a LiDAR-based teacher model to a camera-based student model. To facilitate effective distillation, we introduce Monocular Teaching Assistant Knowledge Distillation (MonoTAKD), which proposes a camera-based teaching assistant (TA) model to transfer robust 3D visual knowledge to the student model, leveraging the smaller feature representation gap. Additionally, we define 3D spatial cues as residual features that capture the differences between the teacher and the TA models. We then leverage these cues to improve the student model's 3D perception capabilities. Experimental results show that our MonoTAKD achieves state-of-the-art performance on the KITTI3D dataset. Furthermore, we evaluate the performance on nuScenes and KITTI raw datasets to demonstrate the generalization of our model to multi-view 3D and unsupervised data settings. Our code is available at https://github.com/hoiliu-0801/MonoTAKD.

cs.CV↗

arXiv2022

Dislocation-based strength model for high energy density conditions

We derive a continuum-level plasticity model for polycrystalline materials in the high energy density regime, based on a single dislocation density and single mobility mechanism, with an evolution model for the dislocation density. The model is formulated explicitly in terms of quantities connected closely with equation of state (EOS) theory, in particular the shear modulus and Einstein temperature, which reduces the number of unconstrained parameters while increasing the range of applicability. The least constrained component is the Peierls barrier $E_P$, which is however accessible by atomistic simulations. We demonstrate an efficient method to estimate the variation of $E_P$ with compression, constrained to fit a single flow stress datum. The formulation for dislocation mobility accounts for some or possibly all of the stiffening at high strain rates usually attributed to phonon drag. The configurational energy of the dislocations is accounted for explicitly, giving a self-consistent calculation of the conversion of plastic work to heat. The configurational energy is predicted to contribute to the mean pressure, and may reach several percent in the terapascal range, which may be significant when inferring scalar EOS data from dynamic loading experiments. The bulk elastic strain energy also contributes to the pressure, but appears to be much smaller. Although inherently describing the plastic relaxation of elastic strain, the model can be manipulated to estimate the flow stress as a function of mass density, temperature, and strain rate, which is convenient to compare with other models and inferences from experiment. The deduced flow stress reproduces systematic trends observed in elastic waves and instability growth experiments, and makes testable predictions of trends versus material and crystal type over a wide range of pressure and strain rate.

cond-mat.mtrl-sci↗