The Kitchin Research Group

In this work, we developed a data-efficient, spin-informed graph neural network framework that augments universal machine-learning interatomic potentials with explicit spin coordinates and initial magnetic-moment guesses, while rigorously preserving the physical symmetries of collinear magnetism. This allows us to predict both the magnitude and direction of atomic spins in bulk and surface materials. By integrating a closed-loop anomaly detection pipeline based on Gaussian mixture models and z-score outlier filtering, we uncovered and corrected mislabeled DFT data, substantially improving dataset quality and model robustness. The resulting SI-GemNet-OC model achieves state-of-the-art accuracy, dramatically speeds up DFT convergence (e.g., reducing SCF cycles for GdDyAl₄ from 211 to 28), and successfully ranks magnetic orderings across hundreds of compounds with a Spearman’s ρ of 0.896. Importantly, we also show that this approach generalizes to complex surface and adsorbate-induced spin configurations, offering a powerful new tool for high-throughput discovery of magnetic materials.

@article{xu-2025-spin-infor,
  author =       {Wenbin Xu and Rohan Yuri Sanspeur and Adeesh Kolluru and Bowen
                  Deng and Peter Harrington and Steven Farrell and Karsten
                  Reuter and John R. Kitchin },
  title =        {Spin-Informed Universal Graph Neural Networks for Simulating
                  Magnetic Ordering},
  journal =      {Proceedings of the National Academy of Sciences},
  volume =       122,
  number =       27,
  pages =        {e2422973122},
  year =         2025,
  doi =          {10.1073/pnas.2422973122},
  URL =          {https://www.pnas.org/doi/abs/10.1073/pnas.2422973122},
  eprint =       {https://www.pnas.org/doi/pdf/10.1073/pnas.2422973122},
}

Copyright (C) 2025 by John Kitchin. See the License for information about copying.

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We’ve developed a new kind of decision-tree model that’s both smart and practical for tackling tough engineering problems. First, we take raw data and "lift" it into a richer feature space so we can slice it more cleverly including angular shapes. Next, we grow a friendly “hyperplane” tree that splits data along these angled cuts, fitting simple linear models in each branch. The result is a piecewise-linear surrogate that behaves a lot like the real system but runs orders of magnitude faster. Finally, because each piece is just a linear model, we can plug the whole thing straight into an optimizer that finds the very best solution under complex rules. That means we can design chemical processes, heat exchangers, or any engineering system more reliably and sustainably - saving time, energy, and cost.

@article{sunshine-2025-hyper-decis,
  author =       {Ethan M. Sunshine and Carolina Colombo Tedesco and Sneha A.
                  Akhade and Matthew J. McNenly and John R. Kitchin and Carl D.
                  Laird},
  title =        {Hyperplane Decision Trees As Piecewise Linear Surrogate Models
                  for Chemical Process Design},
  journal =      {Computers \& Chemical Engineering},
  volume =       {},
  number =       {},
  pages =        109204,
  year =         2025,
  doi =          {10.1016/j.compchemeng.2025.109204},
  url =          {https://doi.org/10.1016/j.compchemeng.2025.109204},
  DATE_ADDED =   {Wed Jul 9 14:14:17 2025},
}

Copyright (C) 2025 by John Kitchin. See the License for information about copying.

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Org-mode version = 9.8-pre

Inverse problems—where we aim to find inputs that produce a desired output—are notoriously challenging in science and engineering. In this study, I explore how generative AI models can tackle these problems by comparing four approaches: a forward model combined with nonlinear optimization, a backward model using partial least squares regression, and two generative methods based on Gaussian mixture models and diffusion-based flow transformations. Using data from a simple RGB-controlled light sensor, the paper demonstrates that generative models can accurately and flexibly infer input settings for target outputs, with advantages such as uncertainty quantification and the ability to condition on partial outputs. This work showcases the promise of generative modeling in reshaping how we approach inverse problems across disciplines.

@article{kitchin-2025-solvin-inver,
  author =       "Kitchin, John R.",
  title =        {Solving an Inverse Problem With Generative Models},
  journal =      "Digital Discovery",
  pages =        "-",
  year =         2025,
  doi =          "10.1039/D5DD00137D",
  url =          "http://dx.doi.org/10.1039/D5DD00137D",
  abstract =     "Inverse problems{,} where we seek the values of inputs to a
                  model that lead to a desired set of outputs{,} are a
                  challenges subset of problems in science and engineering. In
                  this work we demonstrate the use of two generative AI methods
                  to solve inverse problems. We compare this approach to two
                  more conventional approaches that use a forward model with
                  nonlinear programming{,} and the use of a backward model. We
                  illustrate each method on a dataset obtained from a simple
                  remote instrument that has three inputs: the setting of the
                  red{,} green and blue channels of an RGB LED. We focus on
                  several outputs from a light sensor that measures intensity at
                  445 nm{,} 515 nm{,} 590 nm{,} and 630 nm. The specific problem
                  we solve is identifying inputs that lead to a specific
                  intensity in three of those channels. We show that generative
                  models can be used to solve this kind of inverse problem{,}
                  and they have some advantages over the conventional
                  approaches.",
  publisher =    "RSC",
}

Copyright (C) 2025 by John Kitchin. See the License for information about copying.

org-mode source

Org-mode version = 9.8-pre

In this paper, I introduce Claude-Light, a lightweight self-driving lab prototype built on a Raspberry Pi with an RGB LED and ten-channel photometer, all accessible via a simple REST API and Python library. By demonstrating structured automation—from basic scripting and statistical design of experiments through Gaussian process active learning—and exploring large language models for instrument selection, structured data extraction, function calling, and code generation, I showcase both the opportunities and challenges LLMs bring to lab automation (reproducibility, security, and reliability). Claude-Light lowers the barrier for students and researchers to prototype and test automation and AI-driven experimentation before scaling to full self-driving laboratories.

@article{kitchin-2025-evolv-role,
  author =	 {John R. Kitchin},
  title =	 {The Evolving Role of Programming and LLMs in the Development
                  of Self-Driving Laboratories},
  journal =	 {APL Machine Learning},
  volume =	 3,
  number =	 2,
  pages =	 {026111},
  year =	 2025,
  doi =		 {10.1063/5.0266757},
  url =		 {http://dx.doi.org/10.1063/5.0266757},
  DATE_ADDED =	 {Thu May 1 09:22:44 2025},
}

Copyright (C) 2025 by John Kitchin. See the License for information about copying.

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Org-mode version = 9.8-pre

In our latest paper, we developed a high-throughput method to efficiently determine the critical micelle concentration (CMC) of binary surfactant mixtures using a 96-well plate setup. Instead of relying on traditional regression techniques, we used a physics-informed classification approach based on regular solution theory to identify the micellization boundary. By combining model-driven experimental design with a dye solubilization assay, we mapped out the CMC across mixture compositions and accurately extracted the binary interaction parameter, β. We validated the method using the SDS-C8E4 system, and extended it to an electrolyte-rich environment, showing less than 15–18% deviation from literature values. This approach not only accelerates formulation screening but also lays the groundwork for analyzing more complex surfactant systems in the future.

@article{D5DD00058K,
        author = {Chilkunda, Chetan R and Kitchin, John R. and Tilton, Robert D.},
        doi = {10.1039/D5DD00058K},
        journal = {Digital Discovery},
        pages = {-},
        publisher = {RSC},
        title = {A Classification-based Methodology for the Estimation of Binary Surfactant Critical Micelle Concentrations},
        url = {https://doi.org/10.1039/D5DD00058K},
        year = {2025}}

Copyright (C) 2025 by John Kitchin. See the License for information about copying.

org-mode source

Org-mode version = 9.8-pre