AI 中文总结
研究单层1T-TaS2中电荷密度波相变，结合密度泛函理论、机器学习原子间势、分子动力学等方法，通过基准测试确定有效势，大规模模拟再现相变序列，发现热滞及多畴状态，证明通用MLIPs可为CDW材料有限温度研究提供框架。
AI 中文摘要
1T过渡金属二硫属化物中的电荷密度波（CDW）相源于强电子-声子耦合和伴随的晶格不稳定性。使用传统的第一性原理分子动力学（MD）来捕捉其温度依赖的结构演变具有挑战性，因为需要大的超胞和广泛的有限温度采样。在此，我们结合密度泛函理论（DFT）、通用机器学习原子间势（MLIPs）、MD和温度依赖的有效势声子计算来研究单层1T-TaS2中CDW转变的结构和振动特征。通过与DFT位移能进行基准测试，确定了UMA-s-1p1通用机器学习势具有足够的精度用于后续的有限温度模拟。我们的结果表明，大规模MD模拟再现了实验观察到的从低温大卫之星（SoD）扭曲结构到高温原始六边形结构的相变序列。加热-冷却循环表现出热滞现象，冷却时，系统冻结成多畴状态，其中α和β CDW手性独立成核并持续到最低温度。这些发现表明，经过仔细基准测试的通用MLIPs可以为CDW材料的有限温度研究提供一个可扩展的框架。
英文摘要
Charge-density-wave (CDW) phases in 1T transition-metal dichalcogenides arise from strong electron-phonon coupling and accompanying lattice instabilities. Capturing their temperature-dependent structural evolution using conventional first-principles molecular dynamics (MD) remains challenging because of the large supercells and extensive finite-temperature sampling required. Here, we combine density functional theory (DFT), universal machine-learning interatomic potentials (MLIPs), MD, and temperature-dependent effective potential phonon calculations to investigate the structural and vibrational signatures of CDW transitions in monolayer 1T-TaS2. Benchmarking against DFT displacement energies identifies UMA-s-1p1 universal machine learning potentials with sufficient accuracy for subsequent finite-temperature simulations. Our results show that large-scale MD simulations reproduce the experimentally observed phase transition sequence from the low-temperature Star-of-David (SoD) distorted structure to the high-temperature primitive hexagonal structure, as quantified by the number of Ta atoms attributed to SoDs. Heating-cooling cycles exhibit thermal hysteresis, and upon cooling, the system freezes into a multi-domain state in which α and \b{eta} CDW chiralities nucleate independently and persist to the lowest temperatures. These findings demonstrate that carefully benchmarked universal MLIPs can provide a scalable framework for finite-temperature studies of CDW materials.
