arXiv subjects

Matthew W. Kanan

Publications and source records attributed to Matthew W. Kanan.

4 recordsLinked to original sources

arXiv2026

Induction-heated resonant reactors for electrified thermochemistry

We present induction-heated resonant reactors, a new concept in electrified thermochemistry in which the reactor itself serves as a volumetric electromagnetic resonator heated through resonant wireless power transfer. We use the Swiss roll resonator as a model system and show that it can be designed to support uniform volumetric heating profiles and enhanced heat transfer characteristics, creating opportunities for process intensification in scaled systems. Compared to conventional (i.e., non-resonant) induction heating systems, resonant reactors can achieve exceptionally high system efficiencies through the combination of near-unity power-to-heat efficiencies and low thermal losses, both enabled by the utilization of resonant energy transfer. These concepts demonstrate how the integration of electromagnetic power transduction with thermochemical reaction engineering enables new opportunities for utilizing green electricity in sustainable chemical conversion.

eess.SY↗

arXiv2025

Pushing the limits of one-dimensional NMR spectroscopy for automated structure elucidation using artificial intelligence

One-dimensional NMR spectroscopy is one of the most widely used techniques for the characterization of organic compounds and natural products. For molecules with up to 36 non-hydrogen atoms, the number of possible structures has been estimated to range from $10^{20} - 10^{60}$. The task of determining the structure (formula and connectivity) of a molecule of this size using only its one-dimensional $^1$H and/or $^{13}$C NMR spectrum, i.e. de novo structure generation, thus appears completely intractable. Here we show how it is possible to achieve this task for systems with up to 40 non-hydrogen atoms across the full elemental coverage typically encountered in organic chemistry (C, N, O, H, P, S, Si, B, and the halogens) using a deep learning framework, thus covering a vast portion of the drug-like chemical space. Leveraging insights from natural language processing, we show that our transformer-based architecture predicts the correct molecule with 60.4% accuracy within the first 15 predictions using only the $^1$H and $^{13}$C NMR spectra, thus overcoming the combinatorial growth of the chemical space while also being extensible to experimental data via fine-tuning.

physics.chem-ph↗

arXiv2025

Scale Up Analysis of Inductively Heated Metamaterial Reactors

Inductively heated metamaterial reactors, which utilize an open cell lattice baffle structure as a heating susceptor for magnetic induction, are promising candidates for scaled electrified thermochemical reactor operation due to their ability to support volumetric heating profiles and enhanced heat transfer properties. In this work, we present a systematic scale up analysis of inductive metamaterial reactors where we utilize a combination of analytic modeling, numerical simulations, and experiments to project the capabilities and performance of scaled reactors. We use reverse water gas shift as a model reaction system and show that for reactor configurations featuring a uniform metamaterial susceptor, the total system efficiency increases with scale. However, the throughput of these scaled reactors is limited by radial temperature gradients. We further show this bottleneck can be overcome by tailoring the radial effective conductivity profile of the susceptor, which can enable scaled reactors with nearly ideal plug flow-like capabilities. These concepts provide a pathway towards scaled electrified thermochemical reactors with optimal chemical conversion capabilities.

eess.SY↗

arXiv2024

Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning

Rapid determination of molecular structures can greatly accelerate workflows across many chemical disciplines. However, elucidating structure using only one-dimensional (1D) NMR spectra, the most readily accessible data, remains an extremely challenging problem because of the combinatorial explosion of the number of possible molecules as the number of constituent atoms is increased. Here, we introduce a multitask machine learning framework that predicts the molecular structure (formula and connectivity) of an unknown compound solely based on its 1D 1H and/or 13C NMR spectra. First, we show how a transformer architecture can be constructed to efficiently solve the task, traditionally performed by chemists, of assembling large numbers of molecular fragments into molecular structures. Integrating this capability with a convolutional neural network (CNN), we build an end-to-end model for predicting structure from spectra that is fast and accurate. We demonstrate the effectiveness of this framework on molecules with up to 19 heavy (non-hydrogen) atoms, a size for which there are trillions of possible structures. Without relying on any prior chemical knowledge such as the molecular formula, we show that our approach predicts the exact molecule 69.6% of the time within the first 15 predictions, reducing the search space by up to 11 orders of magnitude.

physics.chem-ph↗