A Hierarchy of Fragment-based Quantum Chemical Models Incorporating Machine Learning for Applications in Nanoscale Systems

印第安纳大学的Krishnan Raghavachari获得了化学学部化学理论、模型和计算方法项目的奖励，他开发了一套结合机器学习的量子化学计算方法，用于纳米级系统的广泛应用。虽然许多精确的方法已经由不同的小组在量子化学中开发出来，但由于其相关的高昂的计算成本，它们的适用性迄今为止仅限于小分子。实现计算效率和准确性是当今量子化学最根本的障碍。Raghavachari提出的新方法旨在满足这种需要，即精确地处理中型和大分子，为纳米级系统的研究提供系统的、经过良好测试的模型。这些方法将结合基于分子碎片、系统纠错和最先进的机器学习的思想，以实现高精度和计算效率，目的是提供新的工具来解决涉及中型到大型分子系统和材料的挑战性问题。计算纳米科学作为一个快速发展的领域，正吸引着学生的兴趣，这些项目有望为下一代计算化学研究人员提供良好的培训平台。为了完成这个项目的目标，Raghavachari博士和他的同事们将在两个不同的研究方向上进行研究。在第一种方法中，他们将开发一个基于连接的层次结构（CBH）的踏脚石模型，为密度泛函理论（DFT）提供系统的误差修正，从而获得与耦合聚类计算相当的精度。这将使用两层或三层模型来完成，其中对小片段进行更精确的计算，以纠正DFT误差并实现化学精度。在第二种方法中，Raghavachari将开发一个通用的计算框架，该框架将基于连接的碎片化与基于图网络的机器学习的优势结合起来，以获得计算能量的亚卡路里精度（“化学精度”）。Raghavachari提出，基于分子片段的节点嵌入将优于大多数机器学习应用中传统使用的大多数分子指纹。新开发的方法有可能为处理涉及纳米级系统的复杂问题提供前所未有的准确性。由此产生的计算工具将以平台独立的方式开发，并应与多个量子化学包一起工作，并将免费提供给其他研究小组使用。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。

Krishnan Raghavachari of Indiana University is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop a set of quantum chemical computational methods incorporating machine learning for broad applications in nanoscale systems. While many accurate methods have previously been developed in quantum chemistry by different groups, their applicability thus far has been limited to small molecules due to their associated prohibitive computational cost. Attaining computational efficiency along with accuracy represents the most fundamental obstacle for quantum chemistry today. The new methods that are being proposed by Raghavachari aim to fill this need to treat medium-sized and large molecules accurately, providing systematic well-tested models to the study of nanoscale systems. The methods will combine ideas based on molecular fragmentation, systematic error-correction, and state-of-the-art machine learning to achieve high accuracy in conjunction with computational efficiency, with the aim of providing new tools to solve challenging problems involving intermediate-sized to large molecular systems and materials. Computational nanoscience, as a rapidly expanding field, is attracting student interest, and these projects are expected to provide an excellent training platform for the next generation of researchers in computational chemistry. In order to accomplish goals of this project, Dr. Raghavachari and coworkers will build on two different lines of research that have been developed in the group. In the first approach, they will develop a stepping-stone model based on Connectivity-based Hierarchy (CBH) to provide systematic error corrections to density functional theory (DFT) to result in accuracy comparable to coupled cluster calculations. This will be done using a two- or three-layer model where more accurate calculations are carried out on small fragments to correct for the DFT errors and achieve chemical accuracy. In the second approach, Raghavachari will develop a general computational framework that unifies the advantages of connectivity-based fragmentation with graph network-based machine learning to attain sub-kcal accuracy (“chemical accuracy”) in the calculated energies. Raghavachari has proposed that node embeddings based on molecular fragments will outperform most molecular fingerprints used traditionally in most machine learning applications. The newly developed methods have the potential to provide unprecedented accuracy for the treatment of complex problems involving nanoscale systems. The resulting computational tools will be developed in a platform-independent manner and should work with multiple quantum chemical packages, and will be made freely available for use by other research groups.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.