在高温清洁能源应用中发现材料的缺陷图神经网络
Matthew D Witman1, Anuj Goyal2,3, Tadashi Ogitsu4
1Sandia National Laboratories, Livermore, CA, USA. mwitman@sandia.gov.
Nature computational science
|January 4, 2024
概括
一个新的图形神经网络模型自动化了对材料发现的缺陷形成预测. 这种方法绕过了复杂的建模,加速了清洁能源应用的研究,例如太阳能热化学生产.
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
- 人工智能的人工智能
背景情况:
- 预测缺陷形成度对于材料发现至关重要,但在计算上昂贵.
- 传统方法需要创建和放松缺陷的原子结构,限制高通量选.
研究的目的:
- 开发一种完全自动化的图形神经网络 (GNN) 方法,用于预测缺陷形成度.
- 通过取代计算密集型密度函数理论 (DFT) 计算来实现能源应用材料的快速选.
主要方法:
- 训练了一个缺陷图形神经网络 (dGNN) 模型,使用DFT参考数据对氧化物中空缺缺陷进行训练.
- 整合了dGNN模型与热力学计算用于自由能源评估.
- 在材料项目数据库中应用dGNN模型来选氧化物.
主要成果:
- 该dGNN模型准确地预测了理想晶体结构的缺陷形成度,消除了对显式缺陷建模的需求.
- 成功将零凯尔文缺陷热量连接到太阳能热化学生产的高温工艺条件.
- 证明了该模型对任意结构的适用性以及对其他缺陷类型的潜在通用性.
结论:
- dGNN方法为材料发现提供了一种计算效率高,自动化的方法.
- 这种技术加速了用于清洁能源应用的新材料的识别.
- dGNN框架具有适应性,可以扩展到各种缺陷类型和高级架构.
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