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New neural network models molecular electronic states for chemistry

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的神经网络架构和训练程序，旨在模拟分子系统的电子基态和激发态。该方法学习电子态哈密顿量的隐式基表示，能够统一处理多个电子态、锥交点和非绝热耦合。该模型在胸腺嘧啶和偶氮苯等光化学系统上进行了训练和评估，准确地再现了基态和激发态的能量和振荡器强度，并研究了关键的分子几何形状。 AI

影响 这项研究可能会加速计算化学和光化学中的模拟。

排序理由 该集群包含一篇详细介绍用于分子模拟的新型神经网络架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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New neural network models molecular electronic states for chemistry

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该集群包含一篇详细介绍用于分子模拟的新型神经网络架构的研究论文。[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) · David Juergens, Martin St\"ohr, Andreas E. Hillers-Bendtsen, O. Jonathan Fajen, Todd J. Mart\'inez ·

    激发态化学的潜在统一光滑哈密顿量

    arXiv:2609.01871v1 Announce Type: cross Abstract: We describe a neural network architecture and training procedure designed to model electronic ground and excited states of arbitrary molecular systems. By indirectly learning a latent, implicit basis representation of the electron…

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