一个用于探索和学习潜在能量表面的自动化框架
Yuanbin Liu1, Joe D Morrow1, Christina Ertural2
1Inorganic Chemistry Laboratory, Department of Chemistry, University of Oxford, Oxford, UK.
Nature communications
|August 18, 2025
概括
开发机器学习的原子间潜能是通过自动复数加速的,自动化框架用于探索和装配潜在能量表面. 这项创新简化了数据生成,克服了计算材料科学的关键瓶.
科学领域:
- 计算材料科学科学 计算材料科学
- 材料建模 材料建模
- 机器学习 机器学习
背景情况:
- 机器学习 (ML) 是材料建模的组成部分,使大规模的原子模拟具有量子力学准确性.
- 开发精确的ML原子间潜力需要高质量的训练数据,但手动数据生成和策划存在重大瓶.
研究的目的:
- 引入一个自动化框架,自动复合,以高效地探索和装配潜在能量表面.
- 提高原子机器学习在计算材料科学中的速度和可访问性.
主要方法:
- 开发了一个开源软件包,Autoplex,用于自动化潜能表面勘探和装配.
- 专注于与现有软件架构和用户友好的计算工作流程的互操作性.
主要成果:
- 在各种系统中展示了自动复杂的功能:-氧,SiO2,晶体和液态水,以及相变存储材料.
- 成功自动生成和策划用于机器学习原子间潜力的训练数据.
结论:
- 自动化显著加速了机器学习的原子间潜力的发展.
- autoplex为材料科学的原子化机器学习中的数据瓶提供了多功能和高效的解决方案.
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