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English(EN) Knowing when to trust machine-learned interatomic potentials

了解何时信任机器学习原子间势

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种名为 PROBE 的新方法来评估机器学习原子间势 (MLIPs) 的可靠性。与现有扩展性差的基于集成的方法不同，PROBE 使用预训练 MLIP 的冻结表示上的紧凑分类器。这种事后技术会生成逐个预测的可靠性概率，该概率可以准确跟踪实际误差，而不会改变原始模型。PROBE 还提供化学上可解释的逐原子重要性图作为副产品。 AI

影响 引入了一种更具可扩展性和准确性的方法来评估 MLIPs 的可信度，有可能提高它们在科学研究中的应用。

排序理由 该集群包含一篇 arXiv 预印本，详细介绍了一种评估机器学习原子间势的新方法。

在 arXiv cs.LG 阅读 →

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了解何时信任机器学习原子间势

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该集群包含一篇 arXiv 预印本，详细介绍了一种评估机器学习原子间势的新方法。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Shams Mehdi, Ilkwon Cho, Olexandr Isayev ·

    了解何时信任机器学习的原子间势

    arXiv:2605.00640v1 Announce Type: new Abstract: Prevailing machine-learned interatomic potential (MLIP) uncertainty-quantification methods rely on ensembles of independently trained backbones. These methods scale unfavorably with foundation-scale MLIPs, and their member-disagreem…

  2. arXiv cs.LG TIER_1 English(EN) · Olexandr Isayev ·

    了解何时信任机器学习的原子间势

    Prevailing machine-learned interatomic potential (MLIP) uncertainty-quantification methods rely on ensembles of independently trained backbones. These methods scale unfavorably with foundation-scale MLIPs, and their member-disagreement signals correlate weakly with per-molecule p…

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