Computational Methods for Ensemble Averaged Surface-Enhanced Raman Scattering

在化学系化学理论、模型和计算方法计划的支持下，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)。表面增强拉曼光谱是一种强大的技术，可以用来获得关于化学反应和分子在表面上的取向的详细信息。建模对于获得这种信息很重要，但由于复杂的界面和需要平均许多不同的分子取向，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，并通过整合到现有的计算化学软件包中向广大科学界传播。使用交互式模拟器，詹森博士将向学生传授纳米概念、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Jensen团队将开发一个超越调和近似的多尺度模型，并允许对分子构型进行采样，以便可以对整体平均表面增强拉曼散射(SERS)进行建模。这项研究将为从根本上理解纳米粒子表面分子的动力学和取向提供新的理论工具，为表征与多相催化、光伏和分子传感相关的界面提供新的理论工具。机器学习方法与有效的多尺度模型相结合，预计将能够模拟包括电磁和化学增强机制的集合平均SERS光谱。开发的计算工具将通过合并到软件包中向广泛的科学社区传播。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.