Counting atoms faster: policy-based nuclear magnetic resonance pulse sequencing for atomic abundance measurement

Rohan Shenoy, Evan Austen Coleman, Hans Gaensbauer, Elsa Olivetti
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:54810-54824, 2025.

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 this Paper

BibTeX
@InProceedings{pmlr-v267-shenoy25a, 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}, pages = {54810--54824}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/shenoy25a/shenoy25a.pdf}, url = {https://proceedings.mlr.press/v267/shenoy25a.html}, 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.} }
Endnote
%0 Conference Paper %T Counting atoms faster: policy-based nuclear magnetic resonance pulse sequencing for atomic abundance measurement %A Rohan Shenoy %A Evan Austen Coleman %A Hans Gaensbauer %A Elsa Olivetti %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-shenoy25a %I PMLR %P 54810--54824 %U https://proceedings.mlr.press/v267/shenoy25a.html %V 267 %X 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.
APA
Shenoy, R., Coleman, E.A., Gaensbauer, H. & Olivetti, E.. (2025). Counting atoms faster: policy-based nuclear magnetic resonance pulse sequencing for atomic abundance measurement. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:54810-54824 Available from https://proceedings.mlr.press/v267/shenoy25a.html.

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