可扩展的机器学习方法用于光诱导的顺序失调阶段过渡,具有ab initio准确性
Andrea Corradini1, Giovanni Marini1, Matteo Calandra1
1Department of Physics, University of Trento, Povo, Italy.
概括
本研究引入了一种机器学习方法,将密度函数理论结合起来,以模拟材料中的光诱导相变. 该方法准确地模拟了光激发的,揭示了与热过程不同的非热融机制.
科学领域:
- 计算材料科学科学 计算材料科学
- 凝聚物质物理学 凝聚物质物理学
- 摄影化学的使用.
背景情况:
- 机器学习 (ML) 准确模拟材料的热性质,但与非热相过渡作斗争.
- 在光激发下准确描述潜在能量表面,力和振动特性是具有挑战性的.
- 模拟光诱导的秩序-混乱转换需要捕捉光激发的电子孔等离子体的影响.
研究的目的:
- 开发一种新的计算方法来模拟光诱导的非热相变.
- 创建可靠的原子间电位,以解释电子孔等离子体对结构性质的影响.
- 为了研究光刺激中非热化的机制.
主要方法:
- 结合受约束密度函数理论 (DFT) 和机器学习 (ML) 来产生原子间潜力.
- 开发了能够捕捉电子孔等离子体对材料特性影响的ML潜力.
- 在使用开发的ML潜力对光激发进行分子动力学 (MD) 模拟.
主要成果:
- ML潜能准确地复制了晶体的声子分散.
- 模拟涉及数万个原子,使得大规模分析.
- 确定了一种软音声模式和双井潜力,在低温下驱动非热融,与第一阶热融不同.
结论:
- 新的DFT-ML方法为模拟光激发材料提供了高度可靠的原子间潜力.
- 这些发现提供了对光诱导的秩序-混乱相位过渡的新理解,与热融不同.
- 这种方法可以进行大规模的,长时间的光诱导相变模拟,并具有初始的准确性.
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