可迁移深度量子蒙特卡洛实现激发态势能面的从头模拟
摘要
Nature Computational Science 发表一种用于分子激发态的可迁移深度学习量子蒙特卡洛方法，通过共享参数的拟设让同一波函数表示覆盖多种分子几何与状态，从而降低势能面建模的成本。论文由 Zeno Schätzle 撰写，DOI 为 10.1038/s43588-026-01065-y。
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正文 · 原文
Nature Computational Science, Published online: 06 November 2026; doi:10.1038/s43588-026-01065-y
Transferable deep-learning quantum Monte Carlo for molecular excited states is introduced. Sharing parameters, an ansatz represents the wavefunction across molecular geometries and states, reducing the cost of modeling potential energy surfaces.
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来源：Nature Computational Science · nature.com
引用原文
Zeno Schätzle. Ab initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo[EB/OL]. (2026-11-06)[访问日期]. https://www.nature.com/articles/s43588-026-01065-y.