克服机器学习中的不准确性 对于离子空位模拟的原子间潜力的实现
Pandu Wisesa1, Wissam A Saidi1,2
1Department of Mechanical Engineering & Materials Science, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.
The journal of physical chemistry letters
|December 18, 2024
概括
深度神经网络潜力 (DNP) 难以准确计算像MgO这样的离子材料中的空隙形成能量. 动量张量潜能为这些系统提供了更可靠的替代方案.
科学领域:
- 计算材料科学科学 计算材料科学
- 材料 信息学 信息学
- 固态化学 固态化学
背景情况:
- 机器学习的原子间潜力,特别是深度神经网络 (DNN),以高精度加速模拟.
- 在描述具有多个氧化状态的原始离子系统方面,DNN非常出色.
研究的目的:
- 评估深度神经网络潜能 (DNP) 的准确性,以计算离子物质MgO中空隙形成能量.
- 为了比较DNP的性能与离子系统的动量张量潜力 (MTP).
主要方法:
- 对MGO进行深度神经网络潜能 (DNP) 的实施和测试.
- 在MGO中使用DNP和MTP计算空隙形成能量.
- 对不同氧化物 (MgO,CuO,AgO) 的离子相互作用强度的DNP误差的分析.
主要成果:
- 对于MgO中空隙形成能量,DNP表现出大约3 eV的显著误差.
- 动量张量潜力 (MTP) 准确地预测了MgO中的空隙形成能量.
- DNPs中的错误与离子相互作用强度相关,MgO中的错误大于离子较少的Cu2O和Ag2O.
结论:
- 当前深度神经网络潜力中使用的描述符可能不足以准确地建模离子系统中的空缺.
- 动量张量电位在描述像MgO这样的离子氧化物中的属性,包括空位形成能量,具有卓越的准确性.
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