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English(EN) Active rejection enables reliable generalization of universal machine-learning interatomic potentials

新AI方法改进通用机器学习原子间势的训练

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种名为自适应多教师路由（ATR）的新方法，以改进通用机器学习原子间势（uMLIPs）的训练。该技术解决了精确计算相关的高计算成本问题，而高计算成本通常会限制训练数据集。ATR使用多个预训练的uMLIPs来智能选择可靠的数据点用于伪标签生成，有效拒绝模型不够自信的结构。这种方法能够以最少的精确标签创建大型、高保真数据集，从而提高分子动力学模拟的性能和动力学鲁棒性。 AI

影响 该方法有望加速材料科学中更精确、更鲁棒的分子动力学模拟的开发和应用。

排序理由 该集群包含一篇详细介绍机器学习原子间势新方法的论文。

在 arXiv cs.LG 阅读 →

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新AI方法改进通用机器学习原子间势的训练

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Mingxiang Luo, Xinnan Mao, Lu Wang, Lei Bai, Feng Ding, Yuqiang Li ·

    主动拒绝实现通用机器学习原子间势的可靠泛化

    arXiv:2607.09456v1 Announce Type: new Abstract: Universal machine learning interatomic potentials (uMLIPs) bridge quantum-mechanical accuracy and large-scale molecular dynamics, but the cost of high-accuracy calculations such as r$^2$SCAN limits training to datasets that remain s…

  2. arXiv cs.LG TIER_1 English(EN) · Yuqiang Li ·

    主动拒绝实现通用机器学习原子间势的可靠泛化

    Universal machine learning interatomic potentials (uMLIPs) bridge quantum-mechanical accuracy and large-scale molecular dynamics, but the cost of high-accuracy calculations such as r$^2$SCAN limits training to datasets that remain small relative to the open materials space. Stron…

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