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

University Of California-Berkeley, Berkeley CA

Investigators

Abstract

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.