在化学空间的数据驱动表示中识别和嵌入可转移性
Tim Gould1, Bun Chan2, Stephen G Dale1,3
1Queensland Micro- and Nanotechnology Centre, Griffith University Nathan Qld 4111 Australia.
Chemical science
|July 19, 2024
概括
我们开发了一个可转移性评估工具 (TAT) 来识别和嵌入可转移的数据用于化学中的机器学习. 这种方法克服了数据策划中的人类偏见,改善了密度函数近似 (DFAs) 的模型概括.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 可转移性对于跨科学学科的模型概括至关重要.
- 复杂的机器学习模型可能会掩盖如何实现或错过数据可转移性.
- 识别和嵌入可转移的培训数据是数据驱动科学建模的关键挑战.
研究的目的:
- 开发用于初始化学建模的可转移训练数据的识别和嵌入方法.
- 解决复杂机器学习模型中理解和改进数据可转移性的挑战.
- 增强化学中数据驱动模型的概括能力.
主要方法:
- 引入一种可转移性评估工具 (TAT),用于初始化学建模.
- 在可控制的数据驱动模型上展示TAT,用于开发密度函数近似 (DFA).
- 分析人类直觉对训练数据策划及其化学偏差的影响.
主要成果:
- 人类在数据策划中的直觉可以引入化学偏差,阻碍数据驱动的DFAs的可转移性.
- TAT成功地确定了影响数据可转移性的关键因素.
- 他们提出了三个可转移性原则,包括可转移多样性的概念.
结论:
- 开发的TAT和确定的可转移性原则提供了改善化学机器学习数据策划的策略.
- 将可转移的多样性嵌入到培训数据中可以增强通用机器学习模型的概括性.
- 这项工作为在科学研究中创建更强大,更可转移的数据驱动模型提供了框架.
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