机器学习辅助的三角形凸船体图的构造
Hugo Rossignol1, Michail Minotakis1, Matteo Cobelli1
1School of Physics and CRANN Institute, Trinity College Dublin, College Green, Dublin 2, Ireland.
Journal of chemical information and modeling
|January 25, 2024
概括
本研究介绍了一种机器学习工作流程,使用光谱邻近分析潜力 (SNAP) 来加速新型三元合金的发现. 它准确地构建凸船体图,以DFT级准确度识别新材料的有前途区域.
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
- 合金发现发现 合金发现
背景情况:
- 发现新的金属间三元合金需要广泛的*ab initio*计算,通常受到计算成本的限制.
- 密度函数理论 (DFT) 提供了准确的预测,但具有很高的计算开销,限制了可以探索的假设相数.
- 凸体船体图对于理解合金稳定性和指导材料发现至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 工作流程,以高效地构建精确的三元凸船体图.
- 突出三元合金组成和结构的区域,这些区域对材料发现有希望.
- 为了减少与合金相位图构造中的传统*ab initio*方法相关的计算负担.
主要方法:
- 将一组ML光谱邻近分析潜力 (SNAP) 集成到计算工作流中.
- 利用现有的二元合金数据来训练SNAP模型并生成三元相原型.
- 从原型生成独特的三元装饰,以形成候选化合物.
- 在DFT计算之前,使用SNAP进行候选结构的预放松和选.
- 使用ML加速工作流构建三元凸船体图.
主要成果:
- 拟议的工作流成功地构建了精确的三元凸船体图,精确度达到DFT级.
- 该工作流有效选候选三元化合物,识别有利的原型.
- 除了现有的二元合金数据之外,还不需要额外的第一原则数据来训练ML替代模型.
- 该方法在Cu-Ag-Au和Mo-Ta-W三元系统上进行了演示,突出了其实际适用性.
结论:
- ML增强的工作流显著加快了三元合金发现和相位图构建的过程.
- 这种方法为构建可靠的凸船体图的纯 * ab initio * 方法提供了一个计算效率高的替代方案.
- 工作流程有效地确定了新型三元金属间合金开发的有前途地区.
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