自主热力学信息的数据库生成用于机器学习的原子间潜力和的应用
Vincent G Fletcher1, Albert P Bartók1,2, Livia B Pártay3
1Department of Physics, University of Warwick, Coventry, UK.
概括
我们使用嵌套采样 (NS) 和密度函数理论 (DFT) 开发了一个自动化框架,以创建强大的机器学习原子间潜力 (MLIP) 模型,用于在极端条件下预测材料特性.
科学领域:
- 计算材料科学科学 计算材料科学
- 物理化学 物理化学
- 机器学习 机器学习
背景情况:
- 在各种条件下准确预测材料相性质对于科学进步至关重要.
- 为机器学习原子间潜力 (MLIP) 模型构建培训数据库的现有方法可能是劳动密集型,可能会引入偏差.
- 需要自动化,独立于知识的方法来生成用于MLIP开发的全面数据集.
研究的目的:
- 为MLIP模型构建培训数据库引入一种新的自动化框架.
- 为了能够在广泛的压力和温度范围内准确预测相位特性.
- 开发一个可通用和可转移的MLIP模型,降低计算成本.
主要方法:
- 利用嵌套采样 (NS) 来探索配置空间并生成热力学相关的配置.
- 从一开始就采用密度函数理论 (DFT) 来评估生成的配置.
- 应用了原子集群扩展 (ACE) 架构,使MLIP模型适合生成的数据库.
- 通过将其应用于 (Mg) 证明了框架的有效性.
主要成果:
- 开发了的MLIP模型,能够准确地描述0-600 GPa和0-8000 K的行为.
- 成功计算了的声子光谱,弹性常数和压力-温度相位图.
- 该框架在MLIP模型生成中展示了稳定性,可转移性和通用性.
- 与传统方法相比,实现了较低的计算成本.
结论:
- 拟议的自动化框架有效地为MLIPs生成高质量的培训数据库.
- 这种方法有助于在极端条件下创建准确可靠的材料模型.
- 该方法为材料科学中的MLIP开发提供了一个无偏见,高效和可扩展的解决方案.
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