研究人员开发了一种名为“最后一层投影回归”（LLPR）的新型主动学习工作流程，以提高机器学习力场（MLFF）的训练和微调效率。LLPR充当一种成本效益高的不确定性估计器，能够识别训练集中至关重要的数据点。与随机选择相比，该方法能够以显著更少的标签实现MLFF的全数据精度，这对于基础模型的微调尤其有利。LLPR还有助于检测非物理数据并自动终止学习循环，从而为材料和生物系统带来更准确的模型。 AI
影响 能够更有效地训练机器学习力场，可能加速材料科学和药物发现。
排序理由 该集群包含一篇详细介绍机器学习力场新方法的论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2607.14486v1 Announce Type: cross Abstract: Machine-learning force fields (MLFFs) are reliable only near their training distribution, making efficient construction of diverse training sets a major bottleneck for both train-from-scratch and foundation fine-tuning workflows. …
Machine-learning force fields (MLFFs) are reliable only near their training distribution, making efficient construction of diverse training sets a major bottleneck for both train-from-scratch and foundation fine-tuning workflows. Active learning can reduce this cost, but standard…