用于机器学习的数据生成 - 原子间潜力和超越
Maksim Kulichenko1, Benjamin Nebgen1, Nicholas Lubbers2
1Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.
Chemical reviews
|November 21, 2024
概括
高质量的训练数据对于化学中可靠的机器学习模型至关重要. 本综述探讨了创建有效数据集的方法,以提高模型性能和适用性.
科学领域:
- 数据驱动的化学.
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
背景情况:
- 机器学习模型正在彻底改变分子性质预测.
- 基于ML的原子间潜力 (MLIP) 能够进行精确的原子级模拟.
- 培训数据质量是MLIP可靠性的主要因素.
研究的目的:
- 审查MLIP培训数据的基本组成部分和完整性.
- 讨论确保模型可扩展性和可转移性的方法.
- 突出构建特定领域培训集的策略.
主要方法:
- 积极学习策略和实施.
- 对于原子化数据采集的不确定性量化.
- 使用改造和替代潜在能量表面获取数据.
- 原子数据采样者的作用.
主要成果:
- 积极学习和不确定性量化增强了数据采集.
- 使用专门的数据采样器生成各种结构.
- 新的方法提高了培训数据的多样性.
- 列出了涵盖关键化学空间的公开数据集.
结论:
- 有效的训练数据构建对于推进数据驱动化学至关重要.
- 讨论的方法提高了MLIP的可靠性和适用性.
- 该审查为研究人员在构建强大的ML模型方面提供了一份指南.
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