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化的深度学习原子间潜力:准确预测机械和热性能

Kai Ren¹, Chao Lv², Yang Wang¹

¹School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.

Physical chemistry chemical physics : PCCP

|October 20, 2025

概括

机器学习准确地模拟酸 (BP),一种高温热电材料. 开发的深潜力 (DP) 准确地预测了BP.

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:

化 (BP) 具有出色的热稳定性和化学惰性,使其成为高温热电应用的有希望的候选者.
机器学习 (ML) 的原子间潜力,特别是基于神经网络的深潜力 (DP),为原子模拟提供了高精度和效率.

研究的目的:

开发和验证基于机器学习的酸 (BP) 深潜力 (DP).
通过训练有素的DP,研究BP的结构和物理特性.

主要方法:

使用机器学习 (ML) 技术训练酸 (BP) 的深潜力 (DP).
原子模拟使用训练有素的DP来调查BP的结构和特性.

主要成果:

经过训练的DP准确地复制了BP的辐射和角分布函数.
使用DP模拟的格子常数和密度与第一原则计算和实验数据有很好的一致性.
该DP有效地模拟了BP的关键物理特性,包括BP的结构,机械和热特性.

结论:

开发的深潜力 (DP) 为模拟化 (BP) 提供了一个准确而有效的工具.
这种基于ML的潜力可以可靠地描述BP的复杂物理性质,支持其作为热电材料的进一步发展.

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