步行者指南培训更多的一般机器学习潜力在异质催化
Chenyu Wu1,2, Changxi Yang2,3, Zhening Fang2
1State Key Laboratory of Coordination Chemistry, Key Laboratory of Mesoscopic Chemistry of MOE, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.
Journal of chemical theory and computation
|February 10, 2026
概括
开发用于催化的一般机器学习潜力 (MLP) 需要多样化的数据,这带来了新的培训挑战. 这项研究用各种数据确定了培训中的病态,并为强大的MLP开发提供了指导.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 机器学习潜力 (MLP) 是异质催化物的先进模拟工具.
- 目前的方法专注于系统特定的应用,限制了通用性.
- 培训一般MLP需要多样化,不平衡数据,提出独特的挑战.
研究的目的:
- 通过使用各种数据集,调查培训一般MLP的挑战和病理.
- 为开发可转移的MLP用于催化提供实际建议.
主要方法:
- 系统地调查使用REICO方法生成数据的培训病理.
- 分析与标准系统特定的MLP培训协议的偏差.
- 对各种数据集的数据清理,模型选择和错误指标的评估.
主要成果:
- 在训练MLP时识别了各种不平衡数据的关键偏差和病态.
- 在一般MLP的标准培训学科和评估逻辑方面表现出挑战.
- 突出了超越系统特定工作流程的专业化方法的需要.
结论:
- 培训通用和可转移的MLP需要解决与各种数据相关的独特挑战.
- 为数据清理,模型选择和错误指标提供的建议提高了MLP的稳定性.
- 为开发可靠的MLP提供指导,适用于更广泛的催化系统.
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