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2025
- T. da Silva, T. Cavignac, T. Cerqueira et al. Machine-learning accelerated prediction of two-dimensional conventional superconductors. Materials Horizons, -, -, (2025)
2024
- A. Aouina, P. Borlido, M. Marques et al. Assessing exchange-correlation functionals for accurate densities of solids. Journal of Chemical Theory and Computation, 20, 10852–10860, (2024)
- A. Loew, H. Wang, T. Cerqueira et al. Training machine learning interatomic potentials for accurate phonon properties. Machine Learning: Science and Technology, 5, 045019, (2024)
- J. Schmidt, T. Cerqueira, A. Romero et al. Improving machine-learning models in materials science through large datasets. Materials Today Physics, 48, 101560, (2024)
- T. Cerqueira, Y. Fang, I. Errea et al. Searching materials space for hydride superconductors at ambient pressure. Advanced Functional Materials, 34, 2404043, (2024)
- M. Evans, J. Bergsma, A. Merkys et al. Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange. Digital Discovery, 3, 1509–1533, (2024)
- H. Wang, T. Rauch, A. Tellez-Mora et al. Exploring flat-band properties in two-dimensional M3QX7 compounds. Physical Chemistry Chemical Physics, 26, 21558–21567, (2024)
- J. Jacobs, H. Wang, M. Marques et al. Ruddlesden–Popper oxyfluorides La2Ni1–xCuxO3F2 (0 ≤ x ≤ 1): Impact of the Ni/Cu ratio on the thermal stability and magnetic properties. Inorganic Chemistry, 63, 11317-11324, (2024)
- M. Seifert, T. Rauch, M. Marques et al. Computational prediction and characterization of CuI-based ternary p-type transparent conductors. Journal of Materials Chemistry C, 12, 8320–8333, (2024)
- J. Jacobs, H. Wang, M. Marques et al. Ruddlesden–Popper oxyfluorides La2Ni1–xCuxO3F2 (0 ≤ x ≤ 1): Impact of the Ni/Cu ratio on the structure. Inorganic Chemistry, 63, 6075–6081, (2024)
- A. Sanna, T. Cerqueira, Y. Fang et al. Prediction of ambient pressure conventional superconductivity above 80 K in hydride compounds. npj Computational Materials, 10, 44, (2024)
- K. Gao, W. Cui, J. Shi et al. Prediction of high-Tc superconductivity in ternary actinium beryllium hydrides at low pressure. Physical Review B, 109, 014501, (2024)
- T. Cerqueira, A. Sanna, M. Marques. Sampling the materials space for conventional superconducting compounds. Advanced Materials, 36, 2307085, (2024)
2023
- H. Wang, A. Huran, M. Marques et al. Two-dimensional noble metal chalcogenides in the frustrated snub-square lattice. The Journal of Physical Chemistry Letters, 14, 9969-9977, (2023)
- S. Lehtola, M. Marques. Reproducibility of density functional approximations: How new functionals should be reported. The Journal of Chemical Physics, 159, 114116, (2023)
- L. Duc Pham, P. Sattler, M. Marques et al. Homogeneous electron liquid in arbitrary dimensions beyond the random phase approximation. New Journal of Physics, 25, 083040, (2023)
- N. Hoffmann, T. Cerqueira, P. Borlido et al. Searching for ductile superconducting Heusler X2YZ compounds. npj Computational Materials, 9, 138, (2023)
- J. Schmidt, N. Hoffmann, H.-C. Weng et al. Machine-learning-assisted determination of the global zero-temperature phase diagram of materials. Advanced Materials, 35, 2210788, (2023)
2022
- H. Kulik, T. Hammerschmidt, J. Schmidt et al. Roadmap on machine learning in electronic structure. Electronic Structure, 4, 023004, (2022)
2021
- H. Wang, S. Botti, M. Marques. Predicting stable crystalline compounds using chemical similarity. npj Computational Materials, 7, 12, (2021)