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.

在化学分区的化学测量和成像（CMI）计划的支持下，佐治亚大学的布兰登·罗塔维拉（Brandon Rotavera）和杰夫·史密斯（Geoff Smith）正在开发新的机器学习工具，以促进从其气相光谱法鉴定分子结构。机器学习模型的目标是95％的精度（基于使用已知结构的模型的验证实验），以预测分子结构的关键细节的信心 - 尤其是对于在化学科学和相关工程应用中很重要的难以捉摸的分子。预计该项目将通过贡献新的数据知识建模工具，从而提供更广泛的科学影响，从而提供预测能力，以支持鉴定对光化学，化学动力学，化学物理学，混合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。基于DATA的计算科学（ML），例如机器学习（ML），为可持续能源技术的持续发展提供了关键的见解，可持续能源技术的持续发展广泛地依赖于了解下一代生物燃料混合物中心的弹性自由基的基本化学机制。这项工作的成功预测了鉴定多功能中间体的能力，包括取代的环状醚，有机氢过氧化物和其他复杂物种。异构体分辨真空紫外线（VUV）光谱是一种尖端工具，可通过差异抽象与质谱法相结合。该项目利用此类测量来开发新的支持数据的ML工具，以推动分子结构的分析和解释。产生的见解将促进与对流层化学，联合化学和其他区域相关的化学物种的检测和识别。具体而言，Rotavera/Smith团队正在努力将先前未分配的VUV受害光谱的元素转换为特定的异构体和/或立体异构体。产生的化学见解可能会使一个人可以将异构体与势能表面上的特定反应途径联系起来，例如，这些势能表面是加速可持续混合组合系统所需的基础数值组合模型。对于该项目，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）深神经网络，（2）增强决策树和（3）支持向量机（SVMS）。此类方法将特别有用，对于识别分子中的官能团，这些官能团在商业上不可用，难以合成，这些奖项反映了NSF的法定任务，并且被认为是值得通过基金会的智力优点和更广泛影响的审查标准通过评估来获得支持的。