在液体Na中O溶解度的温度依赖性,通过使用更正的机器学习潜力对Na (l) -Na2O (s) 接口进行原子模拟:向模拟Na燃烧迈出了一步
1Department of Nuclear Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, South Korea. oda@snu.ac.kr.
Physical chemistry chemical physics : PCCP
|July 27, 2023
概括
机器学习潜力使液燃烧接口的精确原子模拟成为可能. 这一突破实现了接近实验的准确性,超越了未来安全研究的密度函数理论.
科学领域:
- 核工程 核工程是指核工程.
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
背景情况:
- 液态 (Na) 的燃烧在冷快速反应堆中构成重大安全风险.
- 燃烧界面的精确原子模拟非常重要,但对于密度函数理论 (DFT) 等传统方法来说具有挑战性.
研究的目的:
- 开发一种机器学习 (ML) 潜力,准确模拟液体Na和固体氧化 (Na2O) 接口.
- 在模拟接口系统和与燃烧相关的材料特性方面实现实验准确性.
主要方法:
- 开发了一种机器学习时刻张量潜力,使用监督和主动学习进行训练,以达到DFT级准确度.
- 实证校正的应用,以提高ML的潜在准确性到实验水平.
- 接口分子动力学模拟和热力学建模以评估液体Na中的氧气溶解度.
主要成果:
- 精确模拟液体Na和固体Na2O的基本特性.
- 在液体Na (350-900K) 中复制了实验氧气溶解度数据,经过经验校正.
- 评估了Na2O溶液和的温度依赖性,解释了由于效应而增加的氧溶性.
结论:
- 机器学习潜力,经经验校正,可以在接口模拟中实现接近实验的准确性,超过DFT的能力.
- 这种方法为可靠的燃烧过程的原子模拟铺平了道路.
- 这些发现为在高温下液态中氧气溶解机制提供了关键的见解.
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