The Voss group is part of the SUNCAT Center for Interface Science and Catalysis, which is a partnership between the Stanford School of Engineering and SLAC National Accelerator Laboratory. The group develops electronic structure methods with an emphasis on materials and surface science with applications in renewable energy conversion and storage. Within the Ultrafast Catalysis FWP at SLAC, the group simulates X-ray absorption and emission spectra to guide the analysis and interpretation of ultrafast surface chemistry experiments with free electron lasers.

The Voss group.
The group in 2022. From left to right: Han Wang, Johannes Voss, Filippo Balzaretti, and Suman Bhasker Ranganath.

The Voss group develops methods for an accurate computational description of surface chemistry. These developments consist of devising semi-empirical exchange-correlation functionals for density functional theory (DFT) and algorithmic improvements for catalytic rate predictions based on micro-kinetic modeling.

The group develops exchange-correlation (XC) functionals by means of fitting the functional form against benchmark data. The benchmark data consists of both higher level of theory (than DFT, i.e. wave function methods for molecules) and experimental data (for bulk cohesive and elastic properties, surface reactions, etc.) [Brown, Maimaiti, Trepte, Bligaard, and Voss, J. Comput. Chem. 42, 2004 (2021)].

Combined data-scientific and physical constraint approach to semi-empirical exchange-correlation functionals.
Schematic of MCML training approach: experimental and quantum chemistry benchmark data for reaction energetics and bulk cohesive and lattice properties are combined with physical constraints in a meta-generalized model space.

Combining these training data with physical model constraints allows for the optimization of the XC functional towards a transferable functional with improved accuracy for surface and gas phase reaction energies without sacrificing the good description of crystal structures with existing approaches:

Comparison of XC functionals.
Mean absolute and signed errors for XC functional performance on predicting adsorption energies in the ADS41 [Mallikarjun Sharada, Karlsson, Maimaiti, Voss, Bligaard, Phys. Rev. B 100, 035439 (2019)] dataset. From Brown, Maimaiti, Trepte, Bligaard, and Voss, J. Comput. Chem. 42, 2004 (2021). Copyright (2021) Wiley Periodicals LLC.

The resulting multi-purpose, constrained and machine-learned (MCML) functional is available in libxc version 5.1.6 and above.

Using Bayesian statistics, the fitted functionals allow for an inferred estimate of the uncertainty in the computed reaction energetic predictions. For more details on the functional development work see

Overview of ML model for TM oxide reaction energy prediction.
Overview of machine-learning model for prediction of transition metal oxide heat of formation through a combination of GGA and GGA+U simulations with Hubbard U-parameters computed from first principles.

When self-interaction errors are significant due to strong Coulomb interactions, e.g., in the case of transition metal oxides, approaches beyond semi-local DFT are necessary to qualitatively describe the electronic structure of these strongly correlated systems correctly. Here, we have employed the machine-learning technique of genetic programming to devise a model that enables the prediction of transition metal oxide heat of formation and other reaction energies involving both localized and delocalized d-electrons from a combination of GGA and GGA+U simulations with first-principles Hubbard U-parameters [Voss, J. Phys. Commun. 6, 035009 (2022)].

Genetic programming model search and performance for predicting transition metal oxide heat of formation.
Genetic programming model search for prediction of transition metal oxide heat of formation (left) and performance of the best model found (right). Adapted from Voss, J. Phys. Commun. 6, 035009 (2022) (under terms of Creative Commons Attribution 4.0 licence.)

The genetic programming search led to a simple model that does not explicitly depend on the ionic types at the Hubbard-corrected sites and thus allows for computation of transition metal oxide reaction energies in the absence of experimental reference data to fit corrections required in previous approaches.

GLLBsc band structure of monolayer MoS2.
GLLBsc band structure of monolayer MoS2. The DFT band gap has been augmented according to the derivative-discontinuity correction of the GLLBsc formalism. The predicted direct band gap at $\textrm{K}$ of about 2eV agrees well with experiment (exciton binding energy corrections have been neglected). Red color indicates dominant Mo $4d$ and blue S $3s$/$p$ character.

The group also works on ab initio functionals for efficient predictions of spectral properties. In these typically orbital dependent functionals, corrections to the inherent band gap problems of DFT are computed from first principles. More information can be found here:

Schematic depiction of adsorption site dependence of XAS.
Schematic depiction of adsorption site sensitivity of XAS with simulated XAS carbon K edge spectra for CO/Ni(100) in top (left) and bridge (right) sites as studied in [Diesen, Rodrigues, Luntz, Abild-Pedersen, Pettersson, and Voss, AIP Advances 10, 115014 (2020)].

To gain a deeper understanding into catalytic selectivity and intermediate states during heterogeneous catalytic reactions, the group performs X-ray spectroscopical simulations to guide analysis of ultrafast catalysis experiments using free electron lasers. More information can be found here:


Dr. Johannes Voss
Staff Scientist

Dr. Changzhi Ai
Machine-learning models for surface and interfacial chemistry

Dr. Filippo Balzaretti
Tight-binding models for dynamic catalysts

Dr. Suman Bhasker Ranganath
Machine-learning models from high-throughput catalysis simulations

Dr. Xixi Qin
Ultrafast catalysis and surface dynamics

Dr. Kris S. Brown
PhD student
Dr. Vanessa J. Bukas
Dr. Elias Diesen
Dr. Samet Demir
Dr. Hendrik H. Heenen
Dr. Yasheng Maimaiti
Dr. Robert B. Sandberg
PhD Student
Dr. Shaama Mallikarjun Sharada
Dr. Aayush R. Singh
PhD Student
Dr. Egidius W. F. Smeets
Dr. Saskia Stegmaier
Dr. Kai Trepte
Dr. Han Wang
SLAC Research Associate


Support by the U.S. Department of Energy Office of Basic Energy Science to the SUNCAT Center for Interface Science and Catalysis and to the Ultrafast Catalysis FWP at SLAC National Accelerator Laboratory is gratefully acknowledged.