arXiv subjects

Woohyeon Baek

Publications and source records attributed to Woohyeon Baek.

4 recordsLinked to original sources

arXiv2026

Origin of the reaction temperature in solid-state materials synthesis

Temperature plays a crucial role in solid-state materials synthesis, but there is currently no mechanistic theory to explain or predict which temperature is best to conduct a solid-state reaction. Reactions between powder precursors are conventionally assumed to be slow diffusion-limited processes; however, recent in situ experiments show that solid-state reactions can complete in minutes above a critical onset temperature. Here, we present evidence that a transient liquid phase forms above the metastable eutectic temperature, and that this non-equilibrium liquid serves as a fast diffusion medium to intermix precursors and initiate a solid-state reaction. This thermodynamic principle is agnostic to the structure or chemistry of the reactants, and can be applied towards the synthesis and manufacturing of a wide range of complex materials.

cond-mat.mtrl-sci↗

arXiv2026

Time-Temperature-Transformation (TTT) Diagrams to rationalize the nucleation and quenchability of metastable $α$-Li$_3$PS$_4$

$α$-Li$_3$PS$_4$ is a promising solid-state electrolyte with the highest ionic conductivity among its polymorphs. However, its formation presents a thermodynamic paradox: the $α$-phase is the equilibrium phase at high temperature and transforms to the stable $γ$-Li$_3$PS$_4$ polymorph when cooled to room temperature; however, $α$-Li$_3$PS$_4$ can be synthesized and quenched in a metastable state via rapid heating at relatively low temperatures. The origin of this synthesizability and anomalous stability has remained elusive. Here, we resolve this paradox by establishing a comprehensive time-temperature-transformation (TTT) diagram, constructed from a computational temperature-size phase diagram and experimental high-time-resolution isothermal measurements. Our density functional theory calculations reveal that at the nanoscale, the $α$-phase is stabilized by its low surface energy, which drastically lowers the nucleation barrier across a wide temperature range. This size-dependent stabilization is directly visualized using in-situ synchrotron X-ray diffraction and electron microscopy, capturing the rapid nucleation of nano-sized $α$-phase and its subsequent slow transformation. This work presents a generalizable framework that integrates thermodynamic and kinetic factors for understanding nucleation and phase transformation mechanisms, providing a rational strategy for the targeted synthesis of functional metastable materials.

cond-mat.mtrl-sci↗

arXiv2024

Quasicrystal bulk and surface energies from density functional theory

Are quasicrystals stable or metastable? Density functional theory (DFT) is often used to evaluate thermodynamic stability, but quasicrystals are long-range aperiodic and their energies cannot be calculated using conventional ab initio methods. Here, we perform first-principles calculations on quasicrystal nanoparticles of increasing sizes, from which we can directly extrapolate their bulk and surface energies. Using this technique, we determine with high confidence that the icosahedral quasicrystals ScZn7.33 and YbCd5.7 are ground-state phases--revealing that translational symmetry is not a necessary condition for the T = 0 K stability of inorganic solids. Although we find the ScZn7.33 quasicrystal to be thermodynamically stable, we show on a mixed thermodynamic and kinetic phase diagram that its solidification from the melt is nucleation-limited, which illustrates why even stable materials may be kinetically challenging to grow. Our techniques here broadly open the door to first-principles investigations into the structure-bonding-stability relationships of aperiodic materials.

cond-mat.mtrl-sci↗

arXiv2022

L3: Accelerator-Friendly Lossless Image Format for High-Resolution, High-Throughput DNN Training

The training process of deep neural networks (DNNs) is usually pipelined with stages for data preparation on CPUs followed by gradient computation on accelerators like GPUs. In an ideal pipeline, the end-to-end training throughput is eventually limited by the throughput of the accelerator, not by that of data preparation. In the past, the DNN training pipeline achieved a near-optimal throughput by utilizing datasets encoded with a lightweight, lossy image format like JPEG. However, as high-resolution, losslessly-encoded datasets become more popular for applications requiring high accuracy, a performance problem arises in the data preparation stage due to low-throughput image decoding on the CPU. Thus, we propose L3, a custom lightweight, lossless image format for high-resolution, high-throughput DNN training. The decoding process of L3 is effectively parallelized on the accelerator, thus minimizing CPU intervention for data preparation during DNN training. L3 achieves a 9.29x higher data preparation throughput than PNG, the most popular lossless image format, for the Cityscapes dataset on NVIDIA A100 GPU, which leads to 1.71x higher end-to-end training throughput. Compared to JPEG and WebP, two popular lossy image formats, L3 provides up to 1.77x and 2.87x higher end-to-end training throughput for ImageNet, respectively, at equivalent metric performance.

cs.CV↗