The Kitchin Research Group: publication

New publication - Neural network embeddings based similarity search method for atomistic systems

Posted September 12, 2022 at 05:26 PM | categories: news, publication | tags:

Searching for atomic structures in databases is like finding a needle in the haystack. It is difficult to construct a query that finds what you want, without finding nothing, or everything! It is difficult to use atomic coordinates because they are sensitive to translations, rotations and permutations. There are many ways to construct equivalent unit cells that also make it difficult to uniquely query materials.

In this paper we show how to construct queries for atomic structures that allow you to quickly find similar atomic structures. We achieve this by using invariant fingerprint vectors from machine learning models coupled with approximate nearest neighbor vector search algorithms. We apply it to molecules, bulk materials and adsorbates on surfaces. We show how the geometric similarity in found atomic systems leads to better data sets for building new machine learning models, and that the found systems tend to show geometric and electronic structure similarity.

You can read more about this work here (it is Open Access): Yilin Yang, Mingie Liu, John Kitchin, Digital Discovery, 2022, https://doi.org/10.1039/D2DD00055E

@article{yang-2022-neural-networ,
  author =       {Yilin Yang and Mingjie Liu and John R. Kitchin},
  title =        {Neural Network Embeddings Based Similarity Search Method for
                  Atomistic Systems},
  journal =      {Digital Discovery},
  volume =       {},
  number =       {},
  pages =        {},
  year =         2022,
  doi =          {10.1039/d2dd00055e},
  url =          {https://doi.org/10.1039/D2DD00055E},
  DATE_ADDED =   {Mon Sep 12 17:21:30 2022},
}

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New publication - Evaluation of the Degree of Rate Control via Automatic Differentiation

Posted March 07, 2022 at 08:40 AM | categories: news, publication | tags:

Determining which steps in a chemical reaction network are important in controlling the reaction rate is challenging. The degree of rate control is a valuable tool for this, but it requires the derivatives of the reaction rate with respect to rate parameters. In many scenarios we do not have an analytical expression for the reaction rate, and even when we do the derivatives may be tedious to derive and implement. In this work, we show how to use automatic differentiation to address this difficulty, enabling straightforward evaluation of the degree of rate control and sensitivity analysis of complex reaction networks.

@article{yang-2022-evaluat,
  author =       {Yang, Yilin and Achar, Siddarth K. and Kitchin, John R.},
  title =        {Evaluation of the degree of rate control via automatic
                  differentiation},
  journal =      {AIChE Journal},
  volume =       {n/a},
  number =       {n/a},
  pages =        {e17653},
  year =         2022,
  keywords =     {catalysis, reaction kinetics},
  doi =          {10.1002/aic.17653},
  url =          {https://aiche.onlinelibrary.wiley.com/doi/abs/10.1002/aic.17653},
  eprint =       {https://aiche.onlinelibrary.wiley.com/doi/pdf/10.1002/aic.17653},
  abstract =     {Abstract The degree of rate control (DRC) quantitatively
                  identifies the kinetically relevant (sometimes known as
                  rate-limiting) steps of a complex reaction network. This
                  concept relies on derivatives which are commonly implemented
                  numerically, for example, with finite differences (FDs).
                  Numerical derivatives are tedious to implement, and can be
                  problematic, and unstable or unreliable. In this study, we
                  demonstrate the use of automatic differentiation (AD) in the
                  evaluation of the DRC. AD libraries are increasingly available
                  through modern machine learning frameworks. Compared with the
                  FDs, AD provides solutions with higher accuracy with lower
                  computational cost. We demonstrate applications in
                  steady-state and transient kinetics. Furthermore, we
                  illustrate a hybrid local-global sensitivity analysis method,
                  the distributed evaluation of local sensitivity analysis, to
                  assess the importance of kinetic parameters over an uncertain
                  space. This method also benefits from AD to obtain
                  high-quality results efficiently.}
}

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New publication - Model-Specific to Model-General Uncertainty for Physical Properties

Posted March 06, 2022 at 08:18 PM | categories: news, publication | tags:

Updated March 06, 2022 at 08:19 PM

When we fit models to data there are two kinds of uncertainty: the kind that represents uncertainty in the data, e.g. random noise that we cannot fit, and uncertainty in the model, e.g. are we using the right one. With a physics based model, we get model-specific estimates of uncertainty. We show in this paper how to think about and quantify these kinds of errors, and particularly how to use Bayesian models like a Gaussian process to get a model-general error when making predictions about physical properties.

@article{zhan-2022-model-specif,
  author =       {Ni Zhan and John R. Kitchin},
  title =        {Model-Specific To Model-General Uncertainty for Physical
                  Properties},
  journal =      {Industrial \& Engineering Chemistry Research},
  volume =       {nil},
  number =       {nil},
  pages =        {acs.iecr.1c04706},
  year =         2022,
  doi =          {10.1021/acs.iecr.1c04706},
  url =          {http://dx.doi.org/10.1021/acs.iecr.1c04706},
  DATE_ADDED =   {Sun Feb 13 12:08:27 2022},
}

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New publication on segregation in ternary alloy surfaces

Posted January 20, 2022 at 12:44 PM | categories: news, publication | tags:

Updated January 20, 2022 at 12:44 PM

In this paper we combine density functional theory, machine learning, Monte Carlo simulations and experimental data to study segregation in a ternary alloy Cu-Pd-Au surface across the composition space. We found varying agreement and disagreement between tehe simulated and experimental results, and discuss the origins of these. Overall, Au segregates significantly across the composition space, and we learned a lot about the contributions to the discrepancies observed in Cu-Pd segregation and Au-Cu segregration.

Find the paper at https://pubs.acs.org/doi/10.1021/acs.jpcc.1c09647.

@article{yang-2022-simul-segreg,
  author =       {Yilin Yang and Zhitao Guo and Andrew J. Gellman and John R.
                  Kitchin},
  title =        {Simulating Segregation in a Ternary Cu-Pd-Au Alloy With
                  Density Functional Theory, Machine Learning, and Monte Carlo
                  Simulations},
  journal =      {The Journal of Physical Chemistry C},
  volume =       {nil},
  number =       {nil},
  pages =        {acs.jpcc.1c09647},
  year =         2022,
  doi =          {10.1021/acs.jpcc.1c09647},
  url =          {http://dx.doi.org/10.1021/acs.jpcc.1c09647},
  DATE_ADDED =   {Thu Jan 20 12:39:49 2022},
}

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New publication - Origin of the Stokes-Einstein Deviation in Liquid Al-Si

Posted December 18, 2021 at 09:06 AM | categories: news, publication | tags:

In many liquid metal alloys the diffusivity and viscosity are related to each other through the Stokes–Einstein–Sutherland (SES) equation. This is useful because it is difficult to measure these properties in liquid metals, and the correlation can be used in design. Near the melting point, however, this relation often fails. In this work we use molecular dynamics to investigate deviations in the SES for molten Si-Al. We find that the viscosity changes faster than the diffusivity due to the formation of atomic clusters. These clusters cause the SES deviation in this liquid alloy system.

@article{zhan-2021-origin-stokes,
  author =       {Ni Zhan and John R. Kitchin},
  title =        {Origin of the Stokes-Einstein Deviation in Liquid Al-Si},
  journal =      {Molecular Simulation},
  volume =       {},
  number =       {},
  pages =        {1-11},
  year =         2021,
  doi =          {10.1080/08927022.2021.2012572},
  url =          {http://dx.doi.org/10.1080/08927022.2021.2012572},
  DATE_ADDED =   {Fri Dec 17 07:41:39 2021},
}

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