材料原子间潜力的微调基础模型与冷的转移学习
Mariia Radova1,2, Wojciech G Stark1, Connor S Allen2,3
1Department of Chemistry, University of Warwick, Coventry, UK.
概括
微调基础模型与转移学习显著改善了机器学习的原子间潜力. 这种方法以较少的数据实现了高精度,增强了材料模拟.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 机器学习的原子间潜力 (MLIP) 能够实现精确的原子模拟,但需要广泛的训练数据.
- 基础模型具有广泛的适用性,但缺乏关键材料属性的精度.
- 为MLIPs生成高质量的培训数据集在计算上昂贵且具有挑战性.
研究的目的:
- 为了证明基础模型潜能可以通过转移学习实现化学准确性.
- 为了减少训练准确的MLIPs的数据要求.
- 开发一个更有效的模拟工作流程,用于材料发现.
主要方法:
- 微调基础模型潜力使用转移学习与部分结的权重和偏差.
- 将冷转移学习应用于表面反应化学和三级合金特性数据集.
- 开发一种使用转移学习潜力作为基本真理的代孕模型.
主要成果:
- 用10-20%的数据实现了冷转移学习,其准确性与在数千个数据点上从头开始训练的模型相美.
- 微调的基础模型在具有挑战性的数据集中达到化学精度.
- 创建了一个同样准确,但在计算上更高效的替代模型.
结论:
- 使用部分结权重进行转移学习是一种有效的方法,可以实现基础模型潜力的高精度.
- 这种方法显著提高了MLIP中的数据效率和计算效率.
- 拟议的工作流加速了材料模拟和发现.
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