低冷的Al-Ni的均质核化通过机器学习的相互作用潜力融化
Johannes Sandberg1, Thomas Voigtmann2,3, Emilie Devijver4
1Université de Lille, CNRS, Unité Matériaux et Transformations, Lille, France.
The Journal of chemical physics
|December 1, 2025
概括
机器学习潜力使大规模的材料核化模拟成为可能. 对于Al-Ni合金,这揭示了纯Ni和AlNi的独特单步核化路径,与经典模型不同.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 同质核化对于材料固化和微观结构至关重要,但原子模拟受到规模和精度的限制.
- 准确地描述原子间相互作用,特别是在具有化学秩序的合金中,在计算上要求很高.
- 最初的模拟无法达到观测罕见核化事件所需的大规模.
研究的目的:
- 为二元Al-Ni合金开发机器学习潜力,以克服模拟核化的局限性.
- 将这种潜力应用于大规模分子动力学模拟,以研究同质核化过程.
- 研究Al-Ni合金和纯Ni的核化路径,并与现有模型进行比较.
主要方法:
- 为二元Al-Ni合金构建了一个高维神经网络潜力.
- 对实验数据进行了严格的验证,包括扩散,粘度,散射和化温度.
- 使用验证的机器学习潜力进行了大型分子动力学模拟.
主要成果:
- 观察到纯在单个步骤中核化为fcc晶相,与之前的模拟相反.
- 在等原子组合下,Al-Ni合金表现出一个单步核化通路,朝着B2结构发展.
- 这项研究强调了原子相互作用潜能对模拟核化途径的重大影响.
结论:
- 机器学习潜力提供了一种强大的方法,可以准确有效地模拟复杂的材料现象,如核化.
- 观察到的独特的核化路径强调了固化过程对原子间力量的敏感性.
- 研究结果为Al-Ni合金的固化行为提供了洞察力,这与工业应用有关.
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