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詹森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在许多不同的分子取向上求平均值，建模仍然很困难。Lasse詹森教授和他的团队将开发新的计算工具来描述这种光谱学，并将其纳入现有的计算化学软件包，传播给广大的科学界。使用交互式模拟器，詹森博士将教育学生纳米概念，机器学习和基础理论;并强调理论和实验之间的相互作用。在这个项目中，宾夕法尼亚州立大学的詹森团队将开发一个多尺度模型，该模型超越了谐波近似，并允许在分子配置上进行采样，从而可以对系综平均表面增强拉曼散射（Sers）进行建模。拟议的研究将提供新的理论工具，为基本了解纳米颗粒表面上的分子的动力学和取向，用于表征多相催化，光催化和分子传感的相关界面。机器学习方法与高效的多尺度模型的结合有望实现对包括电磁和化学增强机制的整体平均Sers光谱的模拟。该奖项反映了NSF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.