介绍用于原子模拟的机器学习潜力
Fabian L Thiemann1,2, Niamh O'Neill2,3,4, Venkat Kapil3,4,5,6
1IBM Research Europe, Daresbury, Warrington WA4 4AD, United Kingdom.
Journal of physics. Condensed matter : an Institute of Physics journal
|November 22, 2024
概括
机器学习潜力 (MLP) 正在改变原子模拟. 本指南介绍了开发MLP,涵盖描述符,模型和数据,以推进计算科学应用.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 物理 物理学 物理
背景情况:
- 机器学习潜力 (MLP) 显著提升了原子学模拟.
- 许多MLP越来越多地成为计算科学家方法的组成部分.
- 该领域需要可访问的指导来开发和应用这些强大的工具.
研究的目的:
- 为机器学习潜力提供全面的概述和介绍.
- 为开发MLP提供系统指南,包括关键组件和方法.
- 展示MLP开发中的实际应用和最近的进展.
主要方法:
- 对用于MLP开发的化学描述符和回归模型的审查.
- 讨论数据生成和验证策略.
- 探索历史模型 (例如,高维神经网络潜力,高斯近似潜力) 和最近的进展.
主要成果:
- 创建和实施MLP的结构化指南.
- 了解从早期模型到当前最先进的技术的演变.
- 引用专家评价,开源软件和实际例子.
结论:
- 机器学习潜力为原子模拟提供了强大的功能.
- 这项工作降低了研究人员采用和利用MLP的障碍.
- 许多MLP已经准备好在模拟中推动科学发现的边界.
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