通过机器学习加速初始融性质计算:应用于高合金TaVCrWW
Li-Fang Zhu1,2, Fritz Körmann1,2,3, Qing Chen4,5
1Department for Computational Materials Design, Max Planck Institute for Sustainable Materials, Max-Planck-str.1, 40237 Düsseldorf, Germany.
概括
一个新的机器学习潜力显著加快密度函数理论 (DFT) 计算,用于预测耐火材料的融特性. 这种高效的方法可以将计算成本降低80%,从而实现高通量材料的发现.
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
- 热力学是一种热力学.
背景情况:
- 准确预测融特性对于开发先进的耐火材料至关重要.
- 对高温材料的点的实验性确定具有挑战性.
- 使用密度函数理论 (DFT) 的初始自由能量计算是准确的,但在计算上昂贵.
研究的目的:
- 开发一种基于DFT的计算效率高的方法来计算融特性.
- 为了降低与传统的初始分子动力学模拟相关的高计算成本.
- 为了使新型耐火材料的高通量选成为可能.
主要方法:
- 机器学习潜力的整合与DFT计算.
- 用自由能量扰动取代昂贵的热力学集成.
- 对高合金TaVCrW的应用,用于性能计算.
主要成果:
- 与现有方法相比,实现了80%的计算资源减少.
- 成功计算了TaVCrW的化温度,,融合和体积变化.
- 确定了TaVCrW的固体和液体相的热容量.
- 结果显示与CALPHAD外推值值有很好的一致性.
结论:
- 拟议的机器学习辅助的DFT方法提供了一种高效的方法来预测材料融特性.
- 这一进步有助于发现和设计高性能耐火材料.
- 该方法适用于复杂的系统,如多元件合金.
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