Machine Learning Models for Interpreting Molecular Structure from Vacuum Ultraviolet Spectra

在化学系化学测量和成像（CMI）计划的支持下，格鲁吉亚大学的布兰登罗塔维拉和杰夫史密斯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的结构。机器学习模型的目标是95%的准确率（基于使用已知结构模型的验证实验），以提供预测分子结构关键细节的信心-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。该项目预计将通过贡献新的数据信息建模工具来产生更广泛的科学影响，这些工具提供预测能力，以支持识别对光化学，化学动力学，化学物理学，燃烧过程和大气化学重要的分子的创新方法。该项目将为包括退伍军人在内的研究生和本科生提供研究机会。机器学习（ML）等数据驱动的计算科学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下一代生物燃料燃烧核心的难以捉摸的自由基的基本化学机制的理解。这项工作的成功取决于识别多功能中间体的能力，包括取代的环醚，有机氢过氧化物和其他复杂物种。异构体分辨真空紫外（VUV）光谱是通过差分吸收结合质谱检测此类物质的尖端工具。该项目利用这些测量来开发新的数据支持的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探测和识别对流层化学，燃烧化学和其他领域相关的化学物质。具体来说，Rotavera/Smith团队正在努力将以前未分配的VUV吸收光谱的元素转换为特定的异构体和/或立体异构体。由此产生的化学见解可以让一个链接异构体的势能面上的特定反应途径，作为一个例子，支持数值燃烧模型需要加速可持续的混合燃烧系统的设计。在这个项目中，主要研究人员正在使用几种有前途的ML方法来识别官能团和其他分子基序：（1）深度神经网络，（2）提升决策树和（3）支持向量机（SVM）。这种方法将是特别有用的分子中的功能基团，其中真正的标准是不可用的商业，这是困难或不可能synthesized.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.