对的短期债券订单潜力的层次强化学习:与古典效率协调的分析嵌入
Aditya Koneru1,2, Henry Chan2, Valeria Molinero3
1Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States.
Journal of chemical theory and computation
|February 19, 2026
概括
强化学习 (RL) 优化了二氧化的原子间潜力,创造了像Q-Tersoff这样的精确模型. 这种方法增强了对材料的模拟,架起了物理和机器学习的桥梁.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 人工智能的人工智能
背景情况:
- 强化学习 (RL) 为开发原子间潜能提供了一种数据效率高的方法.
- 之前的工作优化了使用RL的双向模型.
- 扩展到债券顺序潜力需要结合三体相互作用.
研究的目的:
- 通过强化学习开发用于的角度感知,短距离的原子间潜力.
- 创建分析模型,弥合基于物理和机器学习的描述.
- 为了探索一个26维的参数空间为21个多态.
主要方法:
- 阶层增强学习工作流程,结合蒙特卡罗树搜索和基于财产的奖励.
- 网格参数,密度,角度和凝聚力的顺序优化.
- 基于特索夫型潜力的Q-特索夫和ML-特索夫模型的开发.
主要成果:
- Q-Tersoff和ML-Tersoff模型准确地复制了低能相的能量排序.
- 与双向模型相比,在捕捉角相关性和无形结构因子方面提高了保真度.
- 模型是数量级快于高维的机器学习潜能.
结论:
- 开发的RL框架提供了一条通用和可解释的通往角度感知潜力的路线.
- 这些模型显示了弹性常数和高能框架的局限性,表明了当前分析形式的边界.
- 这种方法成功地将基于物理和机器学习的酸盐材料描述相结合.
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