基本模型的数据效率微调为第一原则的质量升华度
Harveen Kaur1, Flaviano Della Pia1, Ilyes Batatia2
1Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK. v.kapil@ucl.ac.uk.
Faraday discussions
|September 27, 2024
概括
我们开发了一种数据效率高的机器学习方法,可以准确预测冰聚态等分子晶体的升华度. 这种方法以最小的训练数据实现了高精度,推进了材料科学模拟.
科学领域:
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
- 固态物理 固态物理
背景情况:
- 对分子晶体的升华度的准确预测对于技术应用至关重要.
- 达到 sub-kJ mol-1 的精度需要精确的潜在能量表面和有限温度采样,这构成了重大的计算挑战.
研究的目的:
- 为训练机器学习原子间潜力 (MLIPs) 提供一个数据效率高的协议,以准确预测晶体特性.
- 展示该协议在升华度和冰多态物体的物理性质上的有效性.
主要方法:
- 微调培训MLIPs的基础MACE-MP-0模型.
- 使用一个小的数据集 (几十个结构) 来实现高精度.
- 在随机相近似 (RPA) 级别进行六角冰的初步NPT模拟.
主要成果:
- 在只有几十个训练结构的升华度中,在升华度中达到低于kJ mol-1 的精度.
- 在有限的温度和压力下获得的密度误差小于1%.
- 六角冰的RPA级模拟和实验数据之间显示出良好的一致性.
结论:
- 开发的协议使分子晶体MLIP的准确和数据有效的培训成为可能.
- 这种方法显著降低了实现高精度有限温度模拟的计算成本.
- 这些发现对推进分子晶体的建模具有相关电子结构理论方法的准确性具有前途.
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