使用高效的贝叶斯机器学习原子间潜力的相稳定性的预测
1Tim Taylor Department of Chemical Engineering, Kansas State University, Manhattan, Kansas 66506, United States.
The journal of physical chemistry letters
|February 23, 2024
概括
这项研究揭示了使用先进的贝叶斯原子间潜力的异形的热力学相稳定性. 稀有高斯过程 (SGP) 潜能准确地预测.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 凝聚物质物理学 凝聚物质物理学
背景情况:
- 元素具有不同的热力学特性,呈现出多种各异性质 (α-B,β-B,γ-B).
- 准确预测相位稳定性需要可靠的原子间潜力来进行分子动力学 (MD) 模拟.
- 了解的全方位稳定性对于材料设计和应用至关重要.
研究的目的:
- 为了研究α-B,β-B和γ-B的热力学相稳定性.
- 开发和验证贝叶斯的原子间潜力,用于模拟异性.
- 探索相转换中缺陷和的作用.
主要方法:
- 利用通过稀疏高斯过程 (SGP) 机器学习训练的贝叶斯原子间潜力.
- 在分子动力学 (MD) 模拟中使用SGP潜力用于属性预测.
- 使用飞行中的积极学习生成的模拟数据,用于量子力学的准确性.
主要成果:
- 模拟的相位图 (500-1400 K,0-16 GPa) 与实验数据有很好的一致性.
- 确定B13缺陷对于稳定β-B在700K以下至关重要.
- 证明在更高温度下主导相稳定性超过α-B.
结论:
- SGP潜能为预测的热力学,结构和振动性质提供了量子力学的准确性.
- 该研究成功地使用在无缺陷系统上训练的潜能预测了与缺陷相关的现象.
- 这种方法可以准确地研究结晶相稳定性和固态材料中的过渡.
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