Raposo-Hernández Gema, Pappalardo Rafael R, Réal Florent, Vallet Valérie, Sánchez Marcos Enrique
Department of Physical Chemistry, University of Seville, 41012 Seville, Spain.
Université de Lille, CNRS, UMR 8523-PhLAM, Physique des Lasers, Atomes et Molecules, F-59000 Lille, France.
J Chem Phys. 2024 Oct 14;161(14). doi: 10.1063/5.0228155.
Accurately predicting spectra for heavy elements, often open-shell systems, is a significant challenge typically addressed using a single cluster approach with a fixed coordination number. Developing a realistic model that accounts for temperature effects, variable coordination numbers, and interprets experimental data is even more demanding due to the strong solute-solvent interactions present in solutions of heavy metal cations. This study addresses these challenges by combining multiple methodologies to accurately predict realistic spectra for highly charged metal cations in aqueous media, with a focus on the electronic absorption spectrum of Ce3+ in water. Utilizing highly correlated relativistic quantum mechanical (QM) wavefunctions and structures from molecular dynamics (MD) simulations, we show that the convolution of individual vertical transitions yields excellent agreement with experimental results without the introduction of empirical broadening. Good results are obtained for both the normalized spectrum and that of absolute intensity. The study incorporates a statistical machine learning algorithm, Gaussian Mixture Models-Nuclear Ensemble Approach (GMM-NEA), to convolute individual spectra. The microscopic distribution provided by MD simulations allows us to examine the contributions of the octa- and ennea-hydrate of Ce3+ in water to the final spectrum. In addition, the temperature dependence of the spectrum is theoretically captured by observing the changing population of these hydrate forms with temperature. We also explore an alternative method for obtaining statistically representative structures in a less demanding manner than MD simulations, derived from QM Wigner distributions. The combination of Wigner-sampling and GMM-NEA broadening shows promise for wide application in spectroscopic analysis and predictions, offering a computationally efficient alternative to traditional methods.
准确预测重元素（通常是开壳层体系）的光谱是一项重大挑战，通常采用具有固定配位数的单簇方法来解决。由于重金属阳离子溶液中存在强烈的溶质 - 溶剂相互作用，开发一个能考虑温度效应、可变配位数并解释实验数据的现实模型要求更高。本研究通过结合多种方法来准确预测水介质中高电荷金属阳离子的现实光谱，重点关注水中Ce3+的电子吸收光谱。利用来自分子动力学（MD）模拟的高度相关的相对论量子力学（QM）波函数和结构，我们表明单个垂直跃迁的卷积与实验结果具有出色的一致性，而无需引入经验展宽。对于归一化光谱和绝对强度光谱都获得了良好的结果。该研究采用了一种统计机器学习算法，即高斯混合模型 - 核系综方法（GMM - NEA）来卷积单个光谱。MD模拟提供的微观分布使我们能够研究水中Ce3+的八水合物和九水合物对最终光谱的贡献。此外，通过观察这些水合物形式随温度变化的数量，从理论上捕捉了光谱的温度依赖性。我们还探索了一种比MD模拟要求更低的获取具有统计代表性结构的替代方法，该方法源自QM维格纳分布。维格纳采样和GMM - NEA展宽的结合在光谱分析和预测中显示出广泛应用的前景，为传统方法提供了一种计算效率更高的替代方案。
