Machine Learning Models for Interpreting Molecular Structure from Vacuum Ultraviolet Spectra

在化学系化学测量与成像（CMI）项目的支持下，乔治亚大学的Brandon Rotavera和Geoff Smith正在开发新的机器学习工具，以促进从气相光谱中识别分子结构。机器学习模型的目标准确率为95%（基于使用已知结构模型的验证实验），为预测分子结构的关键细节提供信心，特别是对于化学科学和相关工程应用中重要的难以捉摸的分子。该项目预计将产生更广泛的科学影响，通过提供新的数据建模工具，提供预测能力，支持对光化学、化学动力学、化学物理、燃烧过程和大气化学重要的分子识别的创新方法。该项目将为研究生和本科生，包括退伍军人提供研究机会。数据驱动的计算科学，如机器学习（ML），为可持续能源技术的持续发展提供了重要见解，这些技术广泛依赖于对难以捉摸的自由基的基本化学机制的理解，而这些自由基是下一代生物燃料燃烧的核心。这项工作的成功取决于鉴定多功能中间体的能力，包括取代环醚、有机氢过氧化物和其他复杂物质。异构体分辨真空紫外（VUV）光谱是一种尖端的工具，通过微分吸收与质谱相结合来检测这些物种。该项目利用这些测量来开发新的数据支持ML工具，以推进分子结构的分析和解释。由此产生的见解将有助于检测和识别与对流层化学、燃烧化学和其他领域相关的化学物种。具体来说，Rotavera/Smith团队正在努力将以前未分配的VUV吸收光谱元素转化为特定的异构体和/或立体异构体。由此产生的化学见解可能允许人们将异构体与势能表面上的特定反应途径联系起来，例如，支持加速可持续混合燃烧系统设计所需的数值燃烧模型。在这个项目中，主要研究人员正在使用几种有前途的机器学习方法来识别功能基团和其他分子基序：(1)深度神经网络，(2)增强决策树和(3)支持向量机（svm）。这种方法对于鉴定分子中的官能团特别有用，因为这些官能团在商业上没有真正的标准，而且很难或不可能合成。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 Measurement and Imaging (CMI) Program in the Division of Chemistry, Brandon Rotavera and Geoff Smith at the University of Georgia are developing new machine learning tools to facilitate identification of the structure of molecules from their gas phase spectroscopy. The machine-learning models target 95% accuracy (based on validation experiments using models with known structure), to provide confidence in predicting critical details of molecular structure – particularly for elusive molecules that are important in chemical science and related engineering applications. This project is expected to have broader scientific impact by contributing new data-informed modeling tools that provide predictive capabilities to support innovative methods for the identification of molecules that are important to photochemistry, chemical kinetics, chemical physics, combustion processes, and atmospheric chemistry. The project will provide research opportunities for graduate and undergraduate students, including veterans.Data-enabled computational science such as machine learning (ML) offers critical insights for ongoing development of sustainable energy technologies, which rely extensively on understanding fundamental chemical mechanisms of elusive radicals that are central to next-generation biofuel combustion. Success of this effort is predicated on the ability to identify multi-functional intermediates, including substituted cyclic ethers, organic hydroperoxides, and other complex species. Isomer-resolved vacuum ultraviolet (VUV) spectroscopy is a cutting-edge tool to detect such species via differential absorption coupled with mass spectrometry. This project leverages such measurements to develop new data-enabled ML tools to advance analysis and interpretation of molecular structure. Resulting insights will facilitate detection and recognition of chemical species relevant to tropospheric chemistry, combustion chemistry, and other areas. Specifically, the Rotavera/Smith team is working to convert elements of previously unassigned VUV absorption spectra to specific isomers and/or stereoisomers. Resulting chemical insights may allow one to link isomers to specific reaction pathways on potential energy surfaces that, as an example, underpin numerical combustion models needed to accelerate the design of sustainable hybrid combustion systems. For this project, the principal investigators are using several promising ML methods to identify functional groups and other molecular motifs: (1) deep neural networks, (2) boosted decision trees and (3) support vector machines (SVMs). Such methods will be particularly useful for identifying functional groups in molecules for which authentic standards are not available commercially and which are difficult or impossible to synthesize.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.