合金及其化物的相位图通过在格子图上的神经网络和有限的训练数据
Matthew D Witman1, Norman C Bartelt1, Sanliang Ling2
1Sandia National Laboratories, Livermore, California 94551-0969, United States.
The journal of physical chemistry letters
|February 1, 2024
概括
图形神经网络通过有效预测热力学特性来加速材料的发现. 这种方法绕过了计算上昂贵的方法,使得各种应用的新材料的快速探索成为可能.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 机器学习 机器学习
背景情况:
- 预测材料的热力学特性对于技术应用至关重要,但使用密度函数理论 (DFT) 等传统方法计算成本昂贵.
- 高通量材料建模需要高效的替代模型,但像集群扩张这样的现有方法与复杂的组成作斗争.
- 需要准确高效的预测模型,能够处理复杂的材料系统.
研究的目的:
- 开发一种计算效率高的方法来预测材料的热力学特性.
- 为了实现大规模的材料采样,用于目前难以处理的属性预测.
- 证明开发的方法在优化材料稳定性和储存方面的适用性.
主要方法:
- 利用最小复杂度图形神经网络 (GNN) 模型来预测形成能量.
- 采用GNN来预测理想的 (未放松) 晶体表征的属性,绕过昂贵的DFT放松.
- 在小型数据集上训练有素的GNN,以实现准确的预测和推断能力.
主要成果:
- GNN模型准确地从理想表示中预测DFT放松结构的形成能量.
- 这种方法使得大规模采样能够用小型训练数据集进行热力学性质预测.
- 在优化高合金的热力学稳定性和金属合金中的平原压力方面证明了成功的应用.
结论:
- 图形神经网络为预测材料属性的传统方法提供了一个计算效率高的替代方案.
- 这种GNN方法促进了高通量材料的建模,并加速了新材料的发现.
- 该方法具有多功能性,可应用于各种材料发现和建模挑战.
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