Machine Learning

Bhasker Ranganath, Suman, Filippo Balzaretti, and Johannes Voss. “Optimizing Prediction of Chemical Bonds in Interfacial Dynamics through Local Uncertainty Estimates With Neural Network Ensembles”, J. Chem. Inf. Model., 66 (January 27, 2026): 1394-1405. https://doi.org/10.1021/acs.jcim.5c02083.

Voss, Johannes. “Machine Learning for Accuracy in Density Functional Approximations”, J. Comput. Chem., 45 (April 26, 2024): 1829-45. https://doi.org/10.1002/jcc.27366.

Deo, Shyam, Melissa Kreider, Gaurav Kamat, McKenzie Hubert, José Zamora Zeledón, Lingze Wei, Jesse Matthews, Nathaniel Keyes, Ishaan Singh, Thomas Jaramillo, Frank Abild-Pedersen, Michaela Burke Stevens, Kirsten Winther, and Johannes Voss. “Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance”, ChemPhysChem, March 28, 2024. https://doi.org/10.1002/cphc.202400010.

Trepte, Kai, and Johannes Voss. “Data-Driven and Constrained Optimization of Semi-Local Exchange and Nonlocal Correlation Functionals for Materials and Surface Chemistry”, J. Comput. Chem., 43 (2022): 1104. https://doi.org/10.1002/jcc.26872.

Voss, Johannes. “Hubbard-Corrected Oxide Formation Enthalpies Without Adjustable Parameters”, J. Phys. Commun., 6 (2022): 035009. https://doi.org/10.1088/2399-6528/ac6069.

Brown, Kristopher, Yasheng Maimaiti, Kai Trepte, Thomas Bligaard, and Johannes Voss. “MCML: Combining Physical Constraints With Experimental Data for a Multi-Purpose Meta-Generalized Gradient Approximation”, J. Comput. Chem., 42 (2021): 2004. https://doi.org/10.1002/jcc.26732.

Lundgaard, Keld, Jess Wellendorff, Johannes Voss, Karsten Jacobsen, and Thomas Bligaard. “MBEEF-VdW: Robust Fitting of Error Estimation Density Functionals”, Phys. Rev. B, 93 (2016): 235162. https://doi.org/10.1103/PhysRevB.93.235162.