金纳米颗粒熔化的数据驱动模拟和表征
Data-driven simulation and characterisation of gold nanoparticle melting
摘要：
纳米颗粒热稳定性的模拟和分析是其在技术设备中应用的垫脚石，需要快速准确的力场，以及有效的表征方法。在这项工作中，我们基于从密度泛函理论计算中收集的数据，为金纳米粒子开发了高效，可转移和可解释的机器学习力场。我们用它们来研究不同尺寸（1至6 nm）的金纳米粒子的热力学稳定性，包含多达6266个原子，通过分子动力学模拟关于固-液相变。我们预测纳米粒子的熔化温度与现有的实验数据吻合得很好。此外，我们的特点的固-液相变机制，采用无监督的学习计划来分类本地原子环境。因此，我们提供了一个数据驱动的定义的液体原子排列的内部和表面区域的纳米粒子，并采用它来显示，熔化开始在外层。
英文摘要：
The simulation and analysis of the thermal stability of nanoparticles, a stepping stone towards their application in technological devices, require fast and accurate force fields, in conjunction with effective characterisation methods. In this work, we develop efficient, transferable, and interpretable machine learning force fields for gold nanoparticles based on data gathered from Density Functional Theory calculations. We use them to investigate the thermodynamic stability of gold nanoparticles of different sizes (1 to 6 nm), containing up to 6266 atoms, concerning a solid-liquid phase change through molecular dynamics simulations. We predict nanoparticle melting temperatures in good agreement with available experimental data. Furthermore, we characterize the solid-liquid phase change mechanism employing an unsupervised learning scheme to categorize local atomic environments. We thus provide a data-driven definition of liquid atomic arrangements in the inner and surface regions of a nanoparticle and employ it to show that melting initiates at the outer layers.
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