解决机器学习原子间潜力的身体顺序悖论
Sanggyu Chong1, Tong Jiang2, Michelangelo Domina1
1Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
The Journal of chemical physics
|February 13, 2026
概括
机器学习的原子间潜力 (MLIP) 隐含地学习能量贡献. 这项研究揭示了MLIPs发展自己的身体秩序趋势,影响准确性和可概括性.
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.
- 机器学习 机器学习
背景情况:
- 机器学习的原子间潜力 (MLIPs) 经常将预测分解为体有序的能量贡献.
- 固有的"有效的身体秩序"及其对MLIP准确性的影响仍然不太清楚.
研究的目的:
- 为了研究MLIP如何将能量分解成身体排序的贡献.
- 了解影响MLIPs有效身体秩序的因素.
- 探索身体秩序对MLIP准确性和学习行为的影响.
主要方法:
- 讨论了应用多体膨胀在初始计算中的挑战.
- 在集群数据集上训练了各种MLIP.
- 分析了新出现的身体秩序趋势和模型通用性.
主要成果:
- MLIP表现出一种固有的倾向,即推断出有效的身体秩序趋势.
- 这些趋势取决于ML模型类型和数据集组成.
- 根据身体顺序趋势观察到不同的收和概括性.
结论:
- MLIPs自行确定它们的体有序能量分解.
- 了解这些新出现的趋势对于开发更准确和更可概括的MLIP至关重要.
- 为未来的MLIP开发和应用提供了洞察力.
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