New publication in Journal of Physics Condensed Matter

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The Atomic Simulation Environment is a powerful python library for setting up, running and analyzing molecular simulations. I have been using it and contributing to it since around 2002 when I used the ASE-2 version in Python 1.5! The new ase-3 version is much simpler to use, and much more powerful. This paper describes some of its design principles and capabilities. If you use ASE, please cite this paper!

@article{larsen-2017-atomic-simul,
  author =       {Ask Hjorth Larsen and Jens J{\o}rgen Mortensen and Jakob
                  Blomqvist and Ivano E Castelli and Rune Christensen and
                  Marcin Dułak and Jesper Friis and Michael N Groves and
                  Bj{\o}rk Hammer and Cory Hargus and Eric D Hermes and Paul C
                  Jennings and Peter Bjerre Jensen and James Kermode and John
                  R Kitchin and Esben Leonhard Kolsbjerg and Joseph Kubal and
                  Kristen Kaasbjerg and Steen Lysgaard and J{\'o}n Bergmann
                  Maronsson and Tristan Maxson and Thomas Olsen and Lars
                  Pastewka and Andrew Peterson and Carsten Rostgaard and Jakob
                  Schi{\o}tz and Ole Sch{\"u}tt and Mikkel Strange and Kristian
                  S Thygesen and Tejs Vegge and Lasse Vilhelmsen and Michael
                  Walter and Zhenhua Zeng and Karsten W Jacobsen},
  title =        {The Atomic Simulation Environment-A Python Library for Working
                  With Atoms},
  journal =      {Journal of Physics: Condensed Matter},
  volume =       29,
  number =       27,
  pages =        273002,
  year =         2017,
  url =          {http://stacks.iop.org/0953-8984/29/i=27/a=273002},
  abstract =     {The atomic simulation environment (ASE) is a software package
                  written in the Python programming language with the aim of
                  setting up, steering, and analyzing atomistic simulations. In
                  ASE, tasks are fully scripted in Python. The powerful syntax
                  of Python combined with the NumPy array library make it
                  possible to perform very complex simulation tasks. For
                  example, a sequence of calculations may be performed with the
                  use of a simple 'for-loop' construction. Calculations of
                  energy, forces, stresses and other quantities are performed
                  through interfaces to many external electronic structure codes
                  or force fields using a uniform interface. On top of this
                  calculator interface, ASE provides modules for performing many
                  standard simulation tasks such as structure optimization,
                  molecular dynamics, handling of constraints and performing
                  nudged elastic band calculations.},
}

Copyright (C) 2017 by John Kitchin. See the License for information about copying.

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New publication in Crystal Growth & Design

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Usually, metal oxides grow in a single, most stable crystal structure at a particular set of conditions. For example, TiO2 grows in the rutile structure for a large range of pressure and temperature conditions, but under some conditions it can also grow in the anatase structure. In this paper we show that epitaxial stabilization can be used to influence which crystal structures are observed for the growth of tin oxide. Tin oxide is normally only observed in the rutile structure. We grew tin oxide as an epitaxial film on a poly-crystalline substrate of CoNb2O6 which has an α-PbO2 crystal structure. We found that both rutile and α-PbO2 structures could be found in the film, and that the structure correlated with the orientation of the underlying grains. In other words, the orientation of a substrate can influence the structure of an epitaxial film, enabling one to grow films in crystal structures that may be metastable, and unobtainable in bulk samples.

@article{wittkamper-2017-compet-growt,
  author =       {Wittkamper, Julia and Xu, Zhongnan and Kombaiah, Boopathy and
                  Ram, Farangis and De Graef, Marc and Kitchin, John R. and
                  Rohrer, Gregory S. and Salvador, Paul A.},
  title =        {Competitive Growth of Scrutinyite ($\alpha$-PbO2) and Rutile
                  Polymorphs of \ce{SnO2} on All Orientations of Columbite
                  \ce{CoNb2O6} Substrates},
  journal =      {Crystal Growth \& Design},
  volume =       17,
  number =       7,
  pages =        {3929-3939},
  year =         2017,
  doi =          {10.1021/acs.cgd.7b00569},
  url =          {https://doi.org/10.1021/acs.cgd.7b00569},
  eprint =       { http://dx.doi.org/10.1021/acs.cgd.7b00569 },
}

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New publication in Calphad

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Alloys can have rich, complex phase behavior. Cu-Pd alloys for example show an unusual behavior where a BCC lattice forms for some compositions, even though the alloy is made from two metals that are exclusively FCC in structure! Being able to model and predict this kind of behavior is a major challenge. In this work, we use cluster expansions to model the configurational degrees of freedom in the FCC and BCC lattices and show qualitatively that we can predict the region where the B2 phase (the BCC one) forms. The agreement with experiment is not quantitative though, and we show that part this disagreement is due to the lack of vibrational entropy in the cluster expansion. When we include vibrational entropy, the qualitative agreement improves.

@article{geng-2017-first-princ,
  author =       "Feiyang Geng and Jacob R. Boes and John R. Kitchin",
  title =        {First-Principles Study of the Cu-Pd Phase Diagram},
  journal =      "Calphad ",
  volume =       56,
  pages =        "224 - 229",
  year =         2017,
  doi =          {10.1016/j.calphad.2017.01.009},
  url =
                  {https://doi.org/http://dx.doi.org/10.1016/j.calphad.2017.01.009},
  abstract =     "Abstract The equilibrium phase diagram of a Cu-Pd alloy has
                  been computed using cluster expansion and Monte Carlo
                  simulation methods combined with density functional theory.
                  The computed phase boundaries show basic features that are
                  consistent with the experimentally reported phase diagram.
                  Without vibrational free energy contributions, the
                  order-disorder transition temperature is underestimated by 100
                  K and the critical point is inconsistent with experimental
                  result. The addition of vibrational free energy contributions
                  yields a more qualitatively correct Cu-Pd phase diagram in the
                  Cu rich region. ",
  issn =         "0364-5916",
  keywords =     "Density functional theory",
}

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New publication in Molecular Simulation

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Molecules interact with each other when they adsorb on surfaces and these interactions are coverage dependent. Modeling these interactions is a challenge though, because there are many configurations of adsorbates on the surface, and the surface changes due to the interactions. To mitigate these challenges, one often simplifies the model, e.g. by using a cluster expansion or lattice gas Hamiltonian. These approaches have their own limitations though, and are not that useful for modeling dynamic processes like diffusion. Using molecular potentials enables the dynamic simulations, but not at the same level of accuracy as density functional theory. In this work we use density functional theory to train a neural network, and then use the neural network to model coverage-dependent adsorption isotherms and the dynamics of oxygen diffusion on a Pd(111) surface. We show the neural network can capture the onset of surface oxidation, and that the simulation results have comparable accuracy to the DFT calculations it was trained from.

@article{boes-2017-neural-networ,
  author =       {Jacob R. Boes and John R. Kitchin},
  title =        {Neural Network Predictions of Oxygen Interactions on a Dynamic
                  Pd Surface},
  journal =      {Molecular Simulation},
  pages =        {1-9},
  year =         2017,
  doi =          {10.1080/08927022.2016.1274984},
  url =          {https://doi.org/10.1080/08927022.2016.1274984},
  keywords =     {CBET-1506770},
}

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New publication in J. Phys. Chem. C

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The surface composition of an alloy is rarely the same as the bulk composition due to segregation, and it changes with changing reaction conditions. Segregation is a ubiquitous issue in alloy catalysis, and makes modeling alloy surfaces a challenge, because we need to know the surface composition to model them! In this work, we take a first step in using density functional theory to build a neural network potential that we can use with Monte Carlo simulations to predict the temperature dependent surface composition of an Au-Pd bulk alloy in a vacuum. This approach yielded quantitative predictions in good agreement with experimental measurements over the entire bulk composition range.

@article{boes-2017-model-segreg,
  author =       {Boes, Jacob Russell and Kitchin, John R.},
  title =        {Modeling Segregation on {AuPd}(111) Surfaces With Density
                  Functional Theory and Monte Carlo Simulations},
  journal =      {The Journal of Physical Chemistry C},
  volume =       0,
  number =       {ja},
  pages =        {null},
  year =         2017,
  doi =          {10.1021/acs.jpcc.6b12752},
  url =          {https://doi.org/10.1021/acs.jpcc.6b12752},
  eprint =       { http://dx.doi.org/10.1021/acs.jpcc.6b12752 },
}

Copyright (C) 2017 by John Kitchin. See the License for information about copying.

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