一种深度等价神经网络方法,用于高效的混合密度函数计算
Zechen Tang1, He Li1,2, Peize Lin3,4,5
1State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, 100084, Beijing, China.
Nature communications
|October 11, 2024
概括
DeepH-hybrid是一种新的深度学习方法,使用混合函数准确预测电子结构,克服大规模材料的计算成本. 这加速了材料的发现和分析,包括复杂的Moiré扭曲系统.
科学领域:
- 计算材料科学 计算材料科学
- 量子化学是一种量子化学.
- 机器学习在物理学中的应用
背景情况:
- 混合密度函数计算对于准确的电子结构至关重要,但在计算上昂贵,限制了它们在大型系统中的应用.
- 目前的方法需要耗时的自一致场 (SCF) 代,这对材料发现构成瓶.
- 深度学习方法有可能加速这些计算.
研究的目的:
- 开发一个深度等价神经网络,DeepH-hybrid,用于学习混合功能汉密尔顿人.
- 为了实现大规模材料的准确和高效的电子结构计算.
- 应用该方法来研究复杂的系统,如莫伊尔扭曲材料.
主要方法:
- 开发DeepH-hybrid,一个深度等价神经网络.
- 训练网络直接从材料结构中学习混合功能哈密尔顿式.
- 绕过传统的自我一致的字段代.
- 对可靠性,可转移性和效率进行广泛的实验验证和测试.
主要成果:
- 深H混合体表现出高可靠性,可转移性和计算效率.
- 该方法成功地以混合功能准确度预测电子结构.
- 适用于大型超细胞Moiré扭曲材料,包括魔法角度扭曲双层石墨烯.
- 关于精确交换对这些系统中的平面带的影响的第一项研究.
结论:
- DeepH-hybrid显著降低了混合功能计算的计算成本.
- 该方法可以研究以前无法获得的大型和复杂的材料.
- 这项工作将深度学习扩展到超越传统密度函数理论的电子结构.
- 促进了基于深度学习的先进材料科学初学者方法的开发.
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