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Updated: May 9, 2025

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神经网络潜力的原子能精度:利用预训练和转移学习
1Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.
Journal of chemical information and modeling
|May 5, 2025
概括
专注于基于机器学习的原子间潜力 (MLIP) 中的原子能预测,可以提高神经网络潜力 (NNP). 这种方法提高了准确性和稳定性,这对于可靠的材料模拟至关重要.
科学领域:
- 计算材料科学科学 计算材料科学
- 机器学习在物理学中的应用
- 量子化学 是一个量子化学.
背景情况:
- 基于机器学习的原子间潜力 (MLIP) 准确地预测潜在能量表面 (PES).
- 在MLIP中,原子能预测的研究不足,其物理意义往往被忽视.
- 原子能预测中的不准确性可能会损害神经网络潜力 (NNP) 的稳定性和可转移性.
研究的目的:
- 研究原子能预测精度对神经网络潜能 (NNP) 性能的影响.
- 为了证明错误取消如何掩盖总能预测中的不准确性.
- 通过专注于原子能预测,开发一种改进的NNP培训方法.
主要方法:
- 验证原子能预测的不准确性,使用模拟在拉力负荷下变形和故障.
- 用经验潜力预训原子能预测.
- 使用密度函数理论 (DFT) 数据的转移学习来增强NNP.
主要成果:
- 原子能预测中的不准确性被证明会降低NNP的稳定性和可转移性.
- 总能预测中的错误取消被确定为原子能错误的掩盖效应.
- 预训和转移学习显著改善了总能量,力和压力的预测.
结论:
- 原子能预测对于开发高质量和可靠的MLIP至关重要.
- 明确地解决原子能预测准确性提高了NNP的性能.
- 拟议的方法提高了材料模拟NNP的稳定性和可转移性.
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