A Δ-learning strategy for interpretation of spectroscopic observables.

IF 2.3 2区 物理与天体物理 Q3 CHEMISTRY, PHYSICAL
Structural Dynamics-Us Pub Date : 2023-11-06 eCollection Date: 2023-11-01 DOI:10.1063/4.0000215
Luke Watson, Thomas Pope, Raphael M Jay, Ambar Banerjee, Philippe Wernet, Thomas J Penfold
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引用次数: 0

Abstract

Accurate computations of experimental observables are essential for interpreting the high information content held within x-ray spectra. However, for complicated systems this can be difficult, a challenge compounded when dynamics becomes important owing to the large number of calculations required to capture the time-evolving observable. While machine learning architectures have been shown to represent a promising approach for rapidly predicting spectral lineshapes, achieving simultaneously accurate and sufficiently comprehensive training data is challenging. Herein, we introduce Δ-learning for x-ray spectroscopy. Instead of directly learning the structure-spectrum relationship, the Δ-model learns the structure dependent difference between a higher and lower level of theory. Consequently, once developed these models can be used to translate spectral shapes obtained from lower levels of theory to mimic those corresponding to higher levels of theory. Ultimately, this achieves accurate simulations with a much reduced computational burden as only the lower level of theory is computed, while the model can instantaneously transform this to a spectrum equivalent to a higher level of theory. Our present model, demonstrated herein, learns the difference between TDDFT(BLYP) and TDDFT(B3LYP) spectra. Its effectiveness is illustrated using simulations of Rh L3-edge spectra tracking the C-H activation of octane by a cyclopentadienyl rhodium carbonyl complex.

Abstract Image

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用于解释光谱可观察性的Δ-学习策略。
实验可观测值的精确计算对于解释x射线光谱中的高信息含量至关重要。然而,对于复杂的系统来说,这可能很困难,当动力学变得重要时,由于需要大量的计算来捕捉时间演变的可观测值,这一挑战变得更加复杂。虽然机器学习架构已被证明是快速预测谱线形状的一种很有前途的方法,但同时获得准确和足够全面的训练数据是一项挑战。在这里,我们介绍了x射线光谱的Δ学习。Δ-模型不是直接学习结构-谱关系,而是学习较高和较低理论水平之间的结构相关差异。因此,一旦开发出这些模型,就可以用来转换从较低理论水平获得的光谱形状,以模拟与较高理论水平相对应的光谱形状。最终,这实现了精确的模拟,大大减少了计算负担,因为只计算较低水平的理论,而模型可以立即将其转换为相当于较高水平理论的频谱。我们目前的模型,在此演示,学习TDDFT(BLYP)和TDDFT(B3LYP)光谱之间的差异。通过模拟Rh L3边缘光谱跟踪环戊二烯基铑羰基络合物对辛烷的C-H活化,说明了其有效性。
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来源期刊
Structural Dynamics-Us
Structural Dynamics-Us CHEMISTRY, PHYSICALPHYSICS, ATOMIC, MOLECU-PHYSICS, ATOMIC, MOLECULAR & CHEMICAL
CiteScore
5.50
自引率
3.60%
发文量
24
审稿时长
16 weeks
期刊介绍: Structural Dynamics focuses on the recent developments in experimental and theoretical methods and techniques that allow a visualization of the electronic and geometric structural changes in real time of chemical, biological, and condensed-matter systems. The community of scientists and engineers working on structural dynamics in such diverse systems often use similar instrumentation and methods. The journal welcomes articles dealing with fundamental problems of electronic and structural dynamics that are tackled by new methods, such as: Time-resolved X-ray and electron diffraction and scattering, Coherent diffractive imaging, Time-resolved X-ray spectroscopies (absorption, emission, resonant inelastic scattering, etc.), Time-resolved electron energy loss spectroscopy (EELS) and electron microscopy, Time-resolved photoelectron spectroscopies (UPS, XPS, ARPES, etc.), Multidimensional spectroscopies in the infrared, the visible and the ultraviolet, Nonlinear spectroscopies in the VUV, the soft and the hard X-ray domains, Theory and computational methods and algorithms for the analysis and description of structuraldynamics and their associated experimental signals. These new methods are enabled by new instrumentation, such as: X-ray free electron lasers, which provide flux, coherence, and time resolution, New sources of ultrashort electron pulses, New sources of ultrashort vacuum ultraviolet (VUV) to hard X-ray pulses, such as high-harmonic generation (HHG) sources or plasma-based sources, New sources of ultrashort infrared and terahertz (THz) radiation, New detectors for X-rays and electrons, New sample handling and delivery schemes, New computational capabilities.
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