EAGER: ADAPT: Machine Learning for the Analysis of Novel Zero-field Nuclear Magnetic Resonance Spectroscopic Data

EAGER：ADAPT：用于分析新型零场核磁共振波谱数据的机器学习

• 批准号: 2231634
• 负责人: Ashok Ajoy
• 金额: $ 30万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2231634&HistoricalAwards=false
• 关键词: EAGER; ADAPT; Machine; Learning; Analysis

With support from the Chemical Theory, Models, and Computational Methods (CTMC) program in the Division of Chemistry and the Office of Multidisciplinary Activities (OMA), Ashok Ajoy of the University of California, Berkeley (UCB) and Eric Jonas of the University of Chicago (U of C) are working to develop new artificial intelligence (AI) methods for interpretation of zero-to-ultralow-field (ZULF) nuclear magnetic resonance (NMR) spectra. NMR spectroscopy is a vital and widely applicable tool for determining the structure of unknown molecules, but traditional NMR systems, which operate at high magnetic fields, are very large and expensive pieces of equipment and consequently are inaccessible for many researchers. Traditional NMR systems also suffer from very low throughput. New compact ZULF-NMR instruments pioneered by Ajoy Lab have the potential to make NMR spectroscopy much more affordable, widely accessible, and high in throughput. However, ZULF-NMR spectral data is very complicated and difficult to interpret. In this project, Jonas Lab will build upon their recent advances in AI techniques for interpretation of chemical spectra and Ajoy Lab will gather a diverse ZULF-NMR dataset under known controlled conditions for training of AI models. Finally, the Jonas Lab team will combine these techniques and data to produce AI models that rapidly determine molecular structure from ZULF-NMR spectra. It is anticipated that these models can be scaled up for automated analysis of dozens of samples simultaneously without human intervention, enabling "robotic" laboratories to autonomously discover novel molecular substances. The Ajoy and Jonas research groups at UCB and the U of C, respectively, will collaborate to solve the spectrum-to-structure problem for zero-to-ultralow-field (ZULF) NMR spectroscopy. ZULF-NMR systems omit the large, expensive, highly homogeneous superconducting magnets used in traditional high-field (HF) NMR systems. This means that ZULF-NMR mainly measures inter-nuclear couplings (J-couplings). The resulting spectra are very complex and difficult to interpret. By treating determination of molecular structure from a ZULF-NMR spectrum as an inverse problem, the Jonas lab will first leverage new developments in graph neural networks to create a forward model that rapidly computes the probable spectrum for a given molecular structure. To produce training data for this forward model, the Ajoy lab will acquire hundreds of new experimental ZULF-NMR spectra for a set of small molecules (up to 32 atoms), and the Jonas group will simulate the spectra for thousands of other molecules using ab initio methods. The Jonas group will then use this "fast forward model" to simulate ZULF-NMR spectra for millions of molecules, and then use these spectra to train the inverse models in two phases: first, the Jonas team will create a model to compute the posterior distribution over spin system parameters given an observed ZULF-NMR spectrum; second, they will create a model that uses those posterior distributions of spin system parameters to estimate molecular structure via deep imitation learning. The Ajoy and Jonas research groups will extensively validate this approach with additional new experimental spectra. The outlined approach has the potential to enable automated ZULF-NMR structure determination in autonomous laboratories, and the low cost of ZULF-NMR instruments may make structure determination and other applications of NMR available to a much broader array of laboratories and novel use cases.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.

在化学理论，模型和计算方法（CTMC）方案的支持下共振（NMR）光谱。 NMR光谱法是用于确定未知分子结构的重要且广泛的工具，但是在高磁场上运行的传统NMR系统是非常昂贵且昂贵的设备，因此对于许多研究人员来说是无法访问的。传统的NMR系统也遭受非常低的吞吐量。由Ajoy Lab开创的新型紧凑型Zulf-NMR仪器具有使NMR光谱法更实惠，广泛访问且吞吐量高的潜力。但是，Zulf-NMR光谱数据非常复杂且难以解释。在这个项目中，乔纳斯实验室将基于他们在AI技术的最新进步来解释化学光谱，而Ajoy Lab将在已知的受控条件下以培训AI模型的已知条件下，收集多样化的Zulf-NMR数据集。最后，乔纳斯实验室团队将结合这些技术和数据，以产生迅速从Zulf-NMR光谱的分子结构的AI模型。可以预料，可以将这些模型比例扩展，以同时对数十个样品进行自动分析，而无需人工干预，从而使“机器人”实验室能够自主发现新型分子物质。 UCB和C的AJOY和JONAS研究小组分别将协作解决零至欧洲群（Zulf）NMR光谱的频谱之间的结构问题。 Zulf-NMR系统忽略了传统高田（HF）NMR系统中使用的大型，昂贵，高度同质的超导磁铁。这意味着Zulf-NMR主要测量核间耦合（J耦合）。最终的光谱非常复杂且难以解释。通过将Zulf-NMR光谱中分子结构的测定视为逆问题，Jonas Lab将首先利用图神经网络中的新发展，以创建一个正向模型，该模型迅速计算给定分子结构的可能光谱。为了生成该正向模型的训练数据，Ajoy Lab将为一组小分子（最多32个原子）获取数百种新的实验Zulf-NMR光谱，Jonas组将使用AB InitiO方法为数千个其他分子模拟光谱。然后，乔纳斯组将使用此“快进模型”来模拟数百万个分子的Zulf-NMR光谱，然后使用这些光谱在两个阶段中训练逆模型：首先，Jonas团队将创建一个模型，以计算旋转系统的后验分布，给定观察到的Zulf-NMR光谱；其次，他们将创建一个模型，该模型使用自旋系统参数的后验分布通过深入的模仿学习来估计分子结构。 Ajoy和Jonas研究小组将通过其他新的实验光谱广泛验证这种方法。概述的方法有可能在自主实验室中实现自动化的Zulf-NMR结构确定，并且Zulf-NMR仪器的低成本可能会确定结构和NMR的其他应用，可用于更广泛的实验室和新颖的用例，这反映了NSF的法定任务和审查的范围。