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English(EN) Distilling latent electrostatics from foundation machine learning interatomic potentials

新方法从AI势中提取静电学

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为潜伏Ewald求和（LES）的方法，用于从基础机器学习原子间势（MLIPs）中提取静电学性质。该技术能够创建更有效的MLIPs，模拟长程相互作用和电响应，这对于许多化学和材料科学模拟至关重要。该研究对源自各种基础MLIPs的LES提炼模型进行了基准测试，证明了它们预测红外光谱和Born有效电荷张量的能力。 AI

影响 通过从现有的AI势中提取静电学性质，实现更高效、物理上更准确的模拟。

排序理由 详细介绍一种新计算方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新方法从AI势中提取静电学

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详细介绍一种新计算方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xiaoyu Wang, Bingqing Cheng ·

    从基础机器学习原子间势中提炼潜伏静电学

    arXiv:2606.15001v1 Announce Type: cross Abstract: Foundation machine learning interatomic potentials (MLIPs) have enabled atomistic simulations across broad regions of chemical and materials space, but many remain computationally expensive and lack explicit electrostatics, limiti…

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