在小型集群中模拟的融过渡与接近DFT精度的能量模拟了DFT精度
Anirudh Krishnadas1,2, Nicholas E Charron2, Rene Fournier1,3
1Department of Physics and Astronomy, York University, Toronto M3J 1P3, Ontario, Canada.
Journal of chemical theory and computation
|November 19, 2025
概括
本研究介绍了一个计算框架,将第一原则计算和机器学习结合起来,以建模原子集群融. 该方法准确地预测了点,揭示了某些集群的高过渡温度.
科学领域:
- 计算物理和化学 计算物理和化学
- 材料科学是一种材料科学.
- 统计力学就是统计力学.
背景情况:
- 在原子集群中模拟类似融化的过渡对于理解材料特性至关重要.
- 传统的方法往往缺乏复杂系统所需的效率或准确性.
研究的目的:
- 开发和验证一种新的计算框架,用于模拟原子集群中类似融化的过渡.
- 为了研究各种团离子和中性团的化行为.
主要方法:
- 结合了全球优化,密度函数理论 (DFT) 能量计算和机器学习的原子间潜力 (MLIP).
- 利用一个Allegro E(3) -equivariant神经网络的潜力来准确地调节能量.
- 采用并行炼蒙特卡洛模拟,以实现高效的建模.
主要成果:
- MLIP的精度达到10 meV/原子或更高.
- 对Na20的模拟验证了该方法与以前的结果相比.
- 研究了Al_n^+ (n=9-16) 集群的点,其中一些超过了大量点.
- 13^- 具有非常高的点,接近2100 K.
结论:
- 开发的框架允许高效,准确的模拟融类过渡.
- 与散装相比,团离子表现出明显的化行为.
- 特定的集群结构,如Al13^-,具有非常高的热稳定性.
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