Johannes Voss is staff scientist at SLAC National Accelerator Laboratory. He leads the data science and electronic structure method efforts at the SUNCAT Center for Interface Science and Catalysis. The team develops machine learning models for the accurate and efficient prediction of catalytic reaction energies and materials properties. These developments include exchange-correlation functionals for computational surface science and efficient beyond density functional theory approaches. The team uses machine learning and first-principles methods to gain an understanding of energy and charge transfer and reaction mechanisms governing the performance of catalysts and battery materials.
Within the Ultrafast Catalysis FWP at SLAC, he leads the efforts of X-ray spectra simulations for understanding of ultrafast surface chemistry as observed using free-electron lasers.

• Density Functional Tight-Binding Models for Band Structures of Transition-Metal Alloys and Surfaces across the d-Block
  F. Balzaretti, J. Voss
  J. Chem. Theory Comput. 20, 7272 (2024)

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• Machine learning for accuracy in density functional approximations
  J. Voss
  J. Comput. Chem. (2024)

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• Interpretable machine learning models for practical antimonate electrocatalyst performance
  S. Deo, M.E. Kreider, G. Kamat, M. Hubert, J.A. Zamora Zeledón, L. Wei, J. Matthews, N. Keyes, I. Singh, T.F. Jaramillo, F. Abild-Pedersen, M. Burke Stevens, K. Winther, J. Voss
  J. Comput. Chem. (2024)

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• Factors Affecting the Electron Conductivity in Single Crystal Li7La3Zr2O12 and Li7P3S11
  S. Demir, A. Tekin, Y.-T. Chan, C. Scheurer, K. Reuter, A.C. Luntz, J. Voss
  ACS Appl. Energy Mater. 7, 2392 (2024)

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• Data-driven and constrained optimization of semi-local exchange and nonlocal correlation functionals for materials and surface chemistry
  K. Trepte, J. Voss
  J. Comput. Chem. 43, 1104 (2022)

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• Hubbard-corrected oxide formation enthalpies without adjustable parameters
  J. Voss
  J. Phys. Commun. 6, 035009 (2022)

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• MCML: Combining physical constraints with experimental data for a multi-purpose meta-generalized gradient approximation
  K. Brown, Y. Maimaiti, K. Trepte, T. Bligaard, J. Voss
  J. Comput. Chem. 42, 2004 (2021)

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• Accuracy of XAS theory for unraveling structural changes of adsorbates: CO on Ni(100)
  E. Diesen, G.L.S. Rodrigues, A.C. Luntz, F. Abild-Pedersen, L.G.M. Pettersson, J. Voss
  AIP Advances 10, 115014 (2020)

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• High-Throughput Screening of Solid-State Li-Ion Conductors Using Lattice-Dynamics Descriptors
  S. Muy, J. Voss, R. Schlem, R. Koerver, S.J. Sedlmaier, F. Maglia, P. Lamp, W.G. Zeier, Y. Shao-Horn
  ISCIENCE 16, 270 (2019)

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• Interfacial challenges in solid-state Li ion batteries
  A.C. Luntz, J. Voss, K. Reuter
  J. Phys. Chem. Lett. 6, 4599 (2015)

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Google Scholar Profile

Johannes Voss is regular guest lecturer at Stanford in electronic structure and heterogeneous catalysis classes (CHEMENG-444-01/ENERGY-256-01 and CHEMENG 142/242).

Topics covered include the basics of density functional theory & beyond and how this method can be applied to predict reaction rates, thermodynamic stabilities, and other materials properties.

For information on the accompanying exercises see the TA-maintained website http://chemeng444.github.io (initiated by Charlie Tsai).

• vossj@slac.stanford.edu
• +1 (650) 926-2156
• SLAC National Accelerator Laboratory
  2575 Sand Hill Road MS 31
  Menlo Park, CA 94025
  USA