参数化经验性原子间潜力,通过不可归还的衍生方法来预测热物理性质:ThO2和UO2的情况
Shuxiang Zhou1, Chao Jiang1, Enda Xiao2
1Idaho National Laboratory, Idaho Falls, ID 83415, United States of America.
概括
这项研究引入了一种通过比较衍生物来训练实证原子间电位 (EIP) 的新方法,改进了对声子和导热等物质性质的预测.
科学领域:
- 材料科学 材料科学 材料科学
- 计算物理 计算物理
- 固态化学 固态化学
背景情况:
- 通过分子动力学模拟来准确预测材料特性,这取决于高质量的原子间潜能.
- 现有的方法往往难以准确地捕捉与音声相关的属性.
研究的目的:
- 为经验性原子间潜能 (EIPs) 开发一种新的训练方法,以优化语音和相关属性预测.
- 提高对ThO2和UO2等材料的分子动力学模拟的准确性.
主要方法:
- 一个基于直接比较EIP和密度函数理论 (DFT) 计算之间的二次和三次不可减小的导数 (ID) 的培训策略.
- 利用空间组对称性,避免在训练过程中多余的信息.
- 优化嵌入式原子方法的潜在参数和核心外相互作用.
主要成果:
- 开发的EIP准确地预测了热物理性质,显示了与DFT的良好一致.
- 新的EIP在预测声子分散和导热性方面超过了现有的潜力.
- 能够准确估计热膨胀和弗伦克尔对形成能量.
结论:
- 拟议的培训方法显著提高了EIP的准确性,以捕获与音声相关的属性.
- 这种方法提供了一种可靠的方法,用于材料科学中的分子动力学模拟,以生成精确的原子间潜力.
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