Counting Atoms Faster: Policy-Based Nuclear Magnetic Resonance Pulse Sequencing for Atomic Abundance Measurement

Abstract

Quantifying the elemental composition of a material is a general scientific challenge with broad relevance to environmental sustainability. Existing techniques for the measurement of atomic abundances generally require laboratory conditions and expensive equipment. As a result, they cannot be deployed in situ without significant capital investment, limiting their proliferation. Measurement techniques based on nuclear magnetic resonance (NMR) hold promise in this setting due to their applicability across the periodic table, their non-destructive manipulation of samples, and their amenability to in silico optimization. In this work, we learn policies to modulate NMR pulses for rapid atomic abundance quantification. Our approach involves three inter-operating agents which (1) rapidly align nuclear spins for measurement, (2) quickly force relaxation to equilibrium, and (3) toggle control between agents (1) and (2) to minimize overall measurement time. To demonstrate this technique, we consider a specific use case of low-magnetic-field carbon-13 quantification for low-cost, portable analysis of foodstuffs and soils. We find significant performance improvements relative to traditional NMR pulse sequencing, and discuss limitations on the applicability of this approach.

Cite

Text

Shenoy et al. "Counting Atoms Faster: Policy-Based Nuclear Magnetic Resonance Pulse Sequencing for Atomic Abundance Measurement." Proceedings of the 42nd International Conference on Machine Learning, 2025.

Markdown

[Shenoy et al. "Counting Atoms Faster: Policy-Based Nuclear Magnetic Resonance Pulse Sequencing for Atomic Abundance Measurement." Proceedings of the 42nd International Conference on Machine Learning, 2025.](https://mlanthology.org/icml/2025/shenoy2025icml-counting/)

BibTeX

@inproceedings{shenoy2025icml-counting,
  title     = {{Counting Atoms Faster: Policy-Based Nuclear Magnetic Resonance Pulse Sequencing for Atomic Abundance Measurement}},
  author    = {Shenoy, Rohan and Coleman, Evan Austen and Gaensbauer, Hans and Olivetti, Elsa},
  booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
  year      = {2025},
  pages     = {54810-54824},
  volume    = {267},
  url       = {https://mlanthology.org/icml/2025/shenoy2025icml-counting/}
}