如何训练神经网络的潜力
Alea Miako Tokita1, Jörg Behler1
1Lehrstuhl für Theoretische Chemie II, Ruhr-Universität Bochum, 44780 Bochum, Germany and Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr, 44780 Bochum, Germany.
The Journal of chemical physics
|December 21, 2023
概括
机器学习潜力 (MLP) 通过提供对能量和力的有效访问,使精确的原子模拟成为可能. 本教程概述了培训可靠MLP的关键步骤,从数据生成到验证,适用于各种MLP类型.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 机器学习潜力 (MLP) 通过实现超出第一原则方法的大规模计算,彻底改变了原子模拟.
- 在适当训练的情况下,MLP可以有效地访问能量和力量,达到与电子结构计算相美的准确性.
- MLP的灵活性需要仔细的构造,培训和验证,以确保可靠的性能.
研究的目的:
- 为培训可靠的机器学习潜力所涉及的关键步骤提供全面的教程.
- 通过数据生成,模型培训和MLPs验证过程指导研究人员.
- 展示一种可通用的程序,用于构建适用于各种MLP类型的高维神经网络潜力.
主要方法:
- 用于培训和验证的数据生成集.
- 机器学习潜力的训练,以高维神经网络潜力为例.
- 训练有素的MLP的验证,以确保准确性和可靠性.
主要成果:
- 创建准确可靠的机器学习潜力的详细程序.
- 在大型原子模拟中展示MLP能力.
- 描述的培训和验证方法在不同MLP架构中的一般适用性.
结论:
- 适当的培训和验证对于实现机器学习潜力的高精度至关重要.
- 概述的方法提供了一个强大的框架,用于开发可靠的MLP,用于各种各样的原子模拟.
- 机器学习潜力代表了计算模拟中的范式转变,弥合了准确性和效率之间的差距.
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