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

在化学系化学理论、模型和计算方法（CTMC）项目和多学科活动办公室（OMA）的支持下，加州大学伯克利分校（UCB）的Ashok Ajoy和芝加哥大学（U of C）的Eric Jonas正在努力开发新的人工智能（AI）方法，用于解释零至超低场（ZULF）核磁共振（NMR）波谱。核磁共振波谱是确定未知分子结构的一种重要且广泛应用的工具，但传统的核磁共振系统在高磁场下工作，设备非常庞大且昂贵，因此许多研究人员无法使用。传统的核磁共振系统的吞吐量也很低。由Ajoy Lab首创的新型紧凑型ZULF-NMR仪器有可能使NMR光谱学更加经济实惠，广泛使用和高通量。然而，ZULF-NMR光谱数据非常复杂，难以解释。在这个项目中，乔纳斯实验室将利用他们在人工智能技术方面的最新进展来解释化学光谱，而阿乔伊实验室将在已知的控制条件下收集不同的ZULF-NMR数据集，用于训练人工智能模型。最后，乔纳斯实验室团队将结合这些技术和数据，生成人工智能模型，从ZULF-NMR光谱中快速确定分子结构。预计这些模型可以扩大规模，在没有人为干预的情况下同时对数十个样品进行自动分析，使“机器人”实验室能够自主发现新的分子物质。UCB和uc的ajjoy和Jonas研究小组将合作解决零到超低场（ZULF）核磁共振波谱的光谱到结构问题。ZULF-NMR系统省去了传统高场（HF） NMR系统中使用的大型、昂贵、高度均匀的超导磁体。这意味着ZULF-NMR主要测量核间耦合（j -耦合）。所得的光谱非常复杂，难以解释。通过将从ZULF-NMR光谱中确定分子结构作为一个逆问题，乔纳斯实验室将首先利用图神经网络的新发展来创建一个正演模型，该模型可以快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实验室将为一组小分子（最多32个原子）获取数百个新的实验ZULF-NMR光谱，Jonas团队将使用从头算方法模拟数千个其他分子的光谱。然后，乔纳斯团队将使用这个“快进模型”来模拟数百万分子的ZULF-NMR光谱，然后使用这些光谱分两个阶段训练逆模型：首先，乔纳斯团队将创建一个模型来计算给定观察到的ZULF-NMR光谱的自旋系统参数的后验分布；其次，他们将创建一个模型，利用自旋系统参数的后验分布，通过深度模仿学习来估计分子结构。阿乔伊和乔纳斯的研究小组将用额外的新实验光谱广泛地验证这种方法。概述的方法有可能在自主实验室中实现ZULF-NMR结构的自动化测定，并且ZULF-NMR仪器的低成本可能使结构测定和NMR的其他应用可用于更广泛的实验室和新用例。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 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.