在应力-应变曲线上训练的机器学习潜力的相位行为:超离子水冰的情况
María Milagros Raimondo Zavaroni1, Filipe Matusalem2, Oscar Samuel Cajahuaringa Macollunco3
1Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Cuyo, Mendoza M5502JMA, Argentina.
The Journal of chemical physics
|December 12, 2025
概括
一个机器学习模型准确地预测了水冰相变,显示了凝聚物质物理学中更广泛应用的潜力. 这个模型这个模型.
科学领域:
- 凝聚物质物理学 凝聚物质物理学
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
背景情况:
- 水冰的高温和高压阶段表现出复杂的行为.
- 精确模拟相位转换对于理解极端条件下的材料特性至关重要.
研究的目的:
- 评估深度潜在机器学习 (DP-ML) 模型的可转移性,该模型以应力-应变数据进行训练.
- 确定与绝缘冰X,超离子冰XVIII及其融化的相位过渡的共存线.
- 探索经过变形训练的ML模型在热力学属性预测方面的潜力.
主要方法:
- 训练DP-ML模型对结晶水冰相的应力-应变曲线进行训练.
- 使用各种自由能量计算技术.
- 分析DP-ML模型对热力学性质的可转移性.
主要成果:
- 该DP-ML模型准确地复制了冰X-XVIII和XVIII-液体过渡的共存线.
- 结果与之前的实验和计算数据有很好的一致性.
- 该模型证明了从机械 (变形) 到热力学性能的可转移性.
结论:
- 经过变形训练的DP-ML模型显示出预测热力学属性的前景.
- 在训练集中包含变形固态是开发ML交互模型的潜在有益策略.
- 该DP-ML模型可用于进一步研究水冰阶段过渡,包括对XVIII-液体过渡的表征.
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