标题： 机器学习得到的势能突出显示了铝纳米颗粒的熔化和凝固 显示英文标题

标题： Machine-learnt potential highlights melting and freezing of aluminium nanoparticles

摘要： 我们通过经典分子动力学模拟研究了铝纳米颗粒的完整热力学循环，跨越从200个原子到11000个原子的宽广尺寸范围。 铝-铝相互作用使用FLARE套件中开发的新贝叶斯力场（BFF）进行建模，这是本领域的一项前沿工具。 我们讨论了数据库需求以包含熔化的纳米液滴，以避免相变时出现非物理行为。 我们的研究表明，结构稳定性可以理解到多达$3~ 10^5$个原子的尺寸。 开发的Al-BFF预测二十面体稳定性范围可达2000个原子，大约2纳米，随后是二十面体稳定区域，直到25000个原子。 超过这个尺寸，预期结构更倾向于面心立方（FCC）形状。 在固定加热/冷却速率为100K/ns的情况下，我们始终观察到滞后回线，其中熔化温度高于与凝固相关的温度。 液体液滴的退火进一步稳定了二十面体结构，将其稳定性范围扩展到5000个原子。 通过分层k-means聚类分析，我们没有发现表面熔化的证据，但观察到轻微的表面冻结迹象。 无论如何，在所有尺寸下，液体液滴的表面显示出局部结构有序性。 显示英文摘要

摘要： We investigated the complete thermodynamic cycle of aluminium nanoparticles through classical molecular dynamics simulations, spanning a wide size range from 200 atoms to 11000 atoms. The aluminium-aluminium interactions are modelled using a newly developed Bayesian Force Field (BFF) from the FLARE suite, a cutting-edge tool in our field. We discuss the database requirements to include melted nanodroplets to avoid unphysical behaviour at the phase transition. Our study provides a comprehensive understanding of structural stability up to sizes as large as $3~ 10^5$ atoms. The developed Al-BFF predicts an icosahedral stability range of up to 2000 atoms, approximately 2 nm, followed by a region of stability for decahedra, up to 25000 atoms. Beyond this size, the expected structure favours face-centred cubic (FCC) shapes. At a fixed heating/cooling rate of 100K/ns, we consistently observe a hysteresis loop, where the melting temperatures are higher than those associated with solidification. The annealing of a liquid droplet further stabilizes icosahedral structures, extending their stability range to 5000 atoms. Using a hierarchical k-means clustering, we find no evidence of surface melting but observe some mild indication of surface freezing. In any event, the liquid droplet's surface shows local structural order at all sizes.