Machine Learning Models for Interpreting Molecular Structure from Vacuum Ultraviolet Spectra

从真空紫外光谱解释分子结构的机器学习模型

• 批准号: 2304903
• 负责人: Brandon Rotavera
• 金额: $ 39万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2304903&HistoricalAwards=false
• 关键词: Machine; Learning; Models; Interpreting; Molecular

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.

在化学系化学测量和成像 (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，以促进从气相光谱学习中识别分子结构。模型的目标准确度为 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。通过提供新的基于数据的建模工具来提供预测能力，以支持对光化学、化学动力学、化学物理、燃烧过程和大气化学很重要的分子识别的创新方法，从而产生科学影响。该项目将为研究生提供研究机会。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的见解，这些技术依赖于理解难以捉摸的自由基的基本化学机制，而这些自由基是下一代生物燃料燃烧的核心这一努力的成功。基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质，异构体分辨真空紫外 (VUV) 光谱是一种通过差分吸收与质量相结合来检测此类物质的尖端工具。该项目利用此类测量来开发新的数据支持的机器学习工具，以推进对分子结构的分析和解释，从而促进与对流层相关的化学物质的检测和识别。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能使人们能够将异构体与潜在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的能量表面，主要研究人员正在使用几种有前景的机器学习方法来识别官能团和其他分子基序：(1) 深层。神经网络、(2) 增强决策树和 (3) 支持向量机 (SVM) 此类方法对于识别分子中的官能团特别有用，这些官能团在商业上无法获得可靠的标准，并且很难或不可能合成。授予 NSF 的法定使命，并通过评估反映使用基金会的智力优点和更广泛的影响审查标准，被认为值得支持。