Computational Methods for Ensemble Averaged Surface-Enhanced Raman Scattering

在化学系化学理论、模型和计算方法项目的支持下，宾夕法尼亚州立大学的Lasse Jensen教授将开发新的理论方法来描述系综平均表面增强拉曼散射（SERS）。SERS是一种强大的技术，可用于获得有关化学反应和表面分子取向的详细信息。建模对于获得这些信息很重要，但由于复杂的界面和需要对许多不同的分子取向进行平均，因此建模仍然很困难。Lasse Jensen教授和他的团队将开发新的计算工具来描述这种光谱学，这些工具将通过整合到可用的计算化学软件包中，传播到广泛的科学界。詹森博士将使用交互式模拟器向学生讲授纳米概念、机器学习和基础理论；强调理论与实验的相互作用。在这个项目中，宾夕法尼亚州立大学的Jensen团队将开发一个超越谐波近似的多尺度模型，并允许在分子构型上采样，这样就可以模拟集合平均表面增强拉曼散射（SERS）。所提出的研究将为基本理解纳米颗粒表面分子的动力学和取向提供新的理论工具，用于表征与多相催化、光伏和分子传感相关的界面。将机器学习方法与高效的多尺度模型相结合，有望实现包括电磁和化学增强机制在内的综合平均SERS谱的模拟。开发的计算工具将通过纳入软件包向广泛的科学界传播。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

With support from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry, Professor Lasse Jensen of Pennsylvania State University will develop new theoretical methods to describe ensemble averaged surface-enhanced Raman scattering (SERS). SERS is a powerful technique that can be used to obtain detailed information about chemical reactions and the orientation of molecules on surfaces. Modeling is important to obtain this information but remains difficult due to the complicated interface and the need to average over many different molecular orientations. Professor Lasse Jensen and his team will develop new computational tools to describe this spectroscopy that will be disseminated to the broad scientific community through incorporation into available computational chemistry software packages. Using interactive simulators, Dr. Jensen will educate students on nano-concepts, machine learning and fundamental theory; and emphasize the interplay between theory and experiments. In this project, the Jensen team at Pennsylvania State University will develop a multiscale model that goes beyond the harmonic approximation and allows for sampling over molecular configurations such that ensemble averaged surface-enhanced Raman scattering (SERS) can be modeled. The proposed research will provide new theoretical tools for fundamental understanding of dynamics and orientation of molecules on nanoparticle surfaces for characterizing of interfaces of relevance for heterogeneous catalysis, photovoltaics, and molecular sensing. The combination of a machine-learning approach with an efficient multiscale model is expected to enable simulations of ensemble-averaged SERS spectra that include both electromagnetic and chemical enhancement mechanisms. The developed computational tools will be disseminated to a broad scientific community through incorporation into software packages.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.