Kick Matthias, Alexander Ezra, Beiersdorfer Anton, Van Voorhis Troy
Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
Technical University of Munich, Garching, Germany.
Nat Commun. 2024 Sep 12;15(1):8001. doi: 10.1038/s41467-024-52368-5.
An accurate treatment of electronic spectra in large systems with a technique such as time-dependent density functional theory is computationally challenging. Due to the Nyquist sampling theorem, direct real-time simulations must be prohibitively long to achieve suitably sharp resolution in frequency space. Super-resolution techniques such as compressed sensing and MUSIC assume only a small number of excitations contribute to the spectrum, which fails in large molecular systems where the number of excitations is typically very large. We present an approach that combines exact short-time dynamics with approximate frequency space methods to capture large narrow features embedded in a dense manifold of smaller nearby peaks. We show that our approach can accurately capture narrow features and a broad quasi-continuum of states simultaneously, even when the features overlap in frequency. Our approach is able to reduce the required simulation time to achieve reasonable accuracy by a factor of 20-40 with respect to standard Fourier analysis and shows promise for accurately predicting the whole spectrum of large molecules and materials.
使用诸如含时密度泛函理论等技术对大体系中的电子光谱进行精确处理在计算上具有挑战性。根据奈奎斯特采样定理,直接实时模拟要在频率空间中实现足够清晰的分辨率,其时长必须长得令人望而却步。诸如压缩感知和MUSIC等超分辨率技术假定只有少数激发对光谱有贡献,而这在激发数通常非常大的大分子体系中并不适用。我们提出了一种将精确的短时动力学与近似的频率空间方法相结合的方法,以捕捉嵌入在密集的较小相邻峰流形中的大的窄特征。我们表明,即使这些特征在频率上重叠,我们的方法也能同时准确捕捉窄特征和宽广的准连续态。相对于标准傅里叶分析,我们的方法能够将达到合理精度所需的模拟时间减少20到40倍,并有望准确预测大分子和材料的整个光谱。