A hierarchy of composite quantum chemical models for applications in materials chemistry and nanoscience

来自印第安纳州大学的Krishnan Raghavachari获得了化学理论，模型和计算方法计划的奖项，以开发一套分层量子化学计算方法，用于材料化学和纳米科学中最先进的应用。分层概念涉及在简单系统上使用高度精确的方法，以验证复杂系统的更近似和成本效益高的方法。这种计算方法在理解和优化分子的性质以用于一系列科学和技术应用方面发挥着关键作用。然而，由于与精确方法相关的高计算成本，迄今为止大多数此类应用仅在相对小的分子上进行，并且对于许多应用中所需的复杂系统的精确处理仍然存在重大挑战。正在提出的新方法将满足精确处理中型和大型分子的迫切需要，为材料和纳米级系统的研究提供系统的经过良好测试的模型。计算纳米科学，作为一个迅速扩大的领域，将吸引大量的学生的兴趣，这些项目将提供一个很好的机会，培养下一代的研究人员。在这项工作中，各级学生将接受高级计算技术的培训，包括本科生，研究生和博士后研究人员。为了实现提出的目标，Raghavachari和同事们正在建立两个不同的研究前沿，这些前沿是以前使用NSF资助开发的。在第一部分中，CBH（基于连接性的层次结构），一种用于较大分子的精确热化学方法，正在适应于在DFT（密度泛函理论）中提供系统误差校正，以产生与最先进的耦合簇计算相当的精度，并应用于计算与大生物燃料分子相关的可靠键能。在第二部分中，他们的碎片为基础的复合模型（MIM，分子中的分子）正在开发调查反应势能面，并优化策略，使该方法适用于计算更高阶的光谱特性，如核磁共振化学位移或拉曼光学活性的大分子含有数千个原子。每个开发最初都是独立进行的，然后合并到一起用于前沿应用。这些组合方法为处理涉及材料化学和纳米科学的复杂问题提供了前所未有的准确性。新的计算工具正在以独立于平台的方式开发，可以与多个量子化学包一起工作，并可供其他研究小组广泛使用。该奖项还支持印第安纳州大学的一个暑期项目的工作，该项目的本科生来自历史上服务不足的人群。

Krishnan Raghavachari from Indiana University is supported by an award from the Chemical Theory, Models and Computational Methods program to develop a set of hierarchical quantum chemical computational methods for state-of-the-art applications in materials chemistry and nanoscience. The hierarchical concept involves the use of highly accurate methods on simple systems to validate more approximate and cost-effective methods for complex systems. Such computational methods play a key role in understanding and optimizing the properties of molecules for a range of scientific and technological applications. However, most such applications thus far have been carried out only on relatively small molecules due to the high computational cost associated with accurate methods, and significant challenges remain for the accurate treatment of complex systems that are needed in many applications. The new methods that are being proposed will fill a critical need to treat medium-sized and large molecules accurately, providing systematic well-tested models to the study of materials and nanoscale systems. Computational nanoscience, as a rapidly expanding field, will attract a great deal of student interest, and these projects will provide an excellent opportunity for training the next generation of researchers. Students at all levels will be trained in advanced computational techniques in this work, including undergraduates, graduate students, and postdoctoral researchers. In order to accomplish the proposed goals, Raghavachari and coworkers are building on two different research frontiers that have been previously developed using prior NSF funding. In the first part, the CBH (connectivity-based hierarchy), an accurate thermochemical method for larger molecules, is being adapted to provide systematic error corrections in DFT (density functional theory) to yield accuracy comparable to the state-of-the-art coupled cluster calculations, and applied to calculate reliable bond energies relevant for large biofuel molecules. In the second part, their fragment-based composite model (MIM, Molecules-in-Molecules) is being developed to investigate reactive potential energy surfaces, and to optimize strategies to make the method applicable for calculating higher order spectroscopic properties such as nuclear magnetic resonance chemical shift or Raman optical activity on large molecules containing thousands of atoms. Each development is initially being carried out independently and then being merged together for forefront applications. The combined methods are providing unprecedented accuracy for the treatment of complex problems involving materials chemistry and nanoscience. The new computational tools are being developed in a platform-independent manner and can work with multiple quantum chemical packages, and are being made available for widespread use by other research groups. The award also supports work at a summer program at Indiana University with undergraduate students from historically under-served populations.