New publication - Circumventing data imbalance in magnetic ground state data for magnetic moment predictions

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Modeling magnetic materials with DFT is hard. In this work we develop a machine learning approach to predicting magnetic properties of materials based on their structure. Our two stage model first predicts if a material is magnetic, and then if it is, what the magnetic moments on each atom are. We show this can lead to faster and lower energy DFT solutions.

@article{sanspeur-2024-circum-data,
  author =       {Rohan Yuri Sanspeur and John R Kitchin},
  title =        {Circumventing Data Imbalance in Magnetic Ground State Data for
                  Magnetic Moment Predictions},
  journal =      {Machine Learning: Science and Technology},
  volume =       {5},
  number =       {1},
  pages =        {015023},
  year =         2024,
  doi =          {10.1088/2632-2153/ad23fb},
  url =          {http://dx.doi.org/10.1088/2632-2153/ad23fb},
  DATE_ADDED =   {Tue Feb 6 20:13:47 2024},
}

Copyright (C) 2024 by John Kitchin. See the License for information about copying.

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New publication - Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data Set

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In this work, we show that we can use large graph neural networks to predict transition metal complex energies. We developed an improved dataset at a higher level of theory, and tested models ranging from GemNet-T (best) to SchNet (worst). The model performance saturates with the size of neutral structures, and improves with increasing size of charged structures. Finally, we showed that a pre-trained model from OC20 was even better than training from scratch. This indicates a degree of transferability from heterogeneous catalyst models to homogeneous molecular catalysts.

@article{garrison-2023-apply-large,
  author =       {Garrison, Aaron G. and Heras-Domingo, Javier and Kitchin, John
                  R. and dos Passos Gomes, Gabriel and Ulissi, Zachary W. and
                  Blau, Samuel M.},
  title =        {Applying Large Graph Neural Networks To Predict Transition
                  Metal Complex Energies Using the tmQM\_wB97MV Data Set},
  journal =      {Journal of Chemical Information and Modeling},
  volume =       0,
  number =       0,
  pages =        {null},
  year =         2023,
  doi =          {10.1021/acs.jcim.3c01226},
  URL =          {https://doi.org/10.1021/acs.jcim.3c01226},
  eprint =       {https://doi.org/10.1021/acs.jcim.3c01226},
  note =         {PMID: 38049389},
}

Copyright (C) 2023 by John Kitchin. See the License for information about copying.

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New publication - Chemical Properties from Graph Neural Network-Predicted Electron Densities

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The electron density is one of the most important quantities we use DFT to calculate. It is the foundation of how we compute energy, forces, and many other properties. DFT is expensive though, so in this work we show that we can build a graph neural network that can be used to predict electron densities directly from the atomic coordinates of a system. We show that the predicted densities can also be used to estimate dipole moments and Bader charges.

@article{sunshine-2023-chemic-proper,
  author =       {Sunshine, Ethan M. and Shuaibi, Muhammed and Ulissi, Zachary
                  W. and Kitchin, John R.},
  title =        {Chemical Properties From Graph Neural Network-Predicted
                  Electron Densities},
  journal =      {The Journal of Physical Chemistry C},
  volume =       0,
  number =       0,
  pages =        {null},
  year =         2023,
  doi =          {10.1021/acs.jpcc.3c06157},
  url =          {https://doi.org/10.1021/acs.jpcc.3c06157},
  eprint =       {https://doi.org/10.1021/acs.jpcc.3c06157},
}

Copyright (C) 2023 by John Kitchin. See the License for information about copying.

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New publication - Beyond Independent Error Assumptions in Large GNN Atomistic Models

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In this work we show that prediction errors from graph neural networks for related atomistic systems tend to be correlated, and as a result the differences in energy are more accurate than the absolute energies. This is similar to what is observed in DFT calculations where systematic errors also cancel in differences. We show this quantitatively through differences of systems with systematically different levels of similarity. This article is part of the Special Collection: 2023 JCP Emerging Investigators Special Collection.

@article{ock-2023-beyon-indep,
  author =       {Janghoon Ock and Tian Tian and John Kitchin and Zachary
                  Ulissi},
  title =        {Beyond Independent Error Assumptions in Large {GNN} Atomistic
                  Models},
  journal =      {The Journal of Chemical Physics},
  volume =       158,
  number =       21,
  pages =        {nil},
  year =         2023,
  doi =          {10.1063/5.0151159},
  url =          {http://dx.doi.org/10.1063/5.0151159},
  DATE_ADDED =   {Sun Sep 24 14:53:46 2023},
}

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New publication - Sequential Sampling Methods for Finding Classification Boundaries in Engineering Applications

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We have a new publication out! In this work we show how to use classification algorithms to find boundaries in science and engineering applications. These applications come up all over the place, for example you may want to know what compositions phase separate, and which ones are single phase, or what conditions lead to degradation and which ones don't. You might want to know which operating parameters have desirable properties, and which don't. In this work we show how to efficiently find the boundaries between these regions using active learning and a classifier. We show that this approach is generally better (more accurate and fewer experiments) than doing a dense grid search.

@article{bhat-2023-sequen-sampl,
  author =       {Maya Bhat and John R. Kitchin},
  title =        {Sequential Sampling Methods for Finding Classification
                  Boundaries in Engineering Applications},
  journal =      {Industrial \& Engineering Chemistry Research},
  volume =       {n/a},
  number =       {n/a},
  pages =        {n/a},
  year =         2023,
  doi =          {10.1021/acs.iecr.3c02362},
  url =          {http://dx.doi.org/10.1021/acs.iecr.3c02362}
}

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Copyright (C) 2023 by John Kitchin. See the License for information about copying.

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