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2026
- P. Pires, P. De Breuck, H. Wang et al. Machine learning accelerated exploration of the global high pressure phase diagram. Materials Today, 100B, 103523, (2026)
- T. Bispo Da Silva, H. Wang, T. F. T. Cerqueira et al. High-throughput study of electrical conductivity in ordered metals. npj Computational Materials, 12, 346, (2026)
- P. Pires, P. De Breuck, M. Fava et al. Machine learning materials properties by encoding orbital-projected density of states. arXiv: 2607.07339, xx, xx, (2026)
- H. Wang, A. Vives, M. Marques et al. Strong band bowing in BiOX (X = Cl, Br, I) due to halogen alloying. Physical Chemistry Chemical Physics, 28, 15567–15576, (2026)
- T. Cavignac, J. Schmidt, P. De Breuck et al. AI-driven expansion and application of the Alexandria database. Journal of Physics: Materials, 9, 025014, (2026)
- P. Pires, T. Bispo Da Silva, K. Gao et al. Machine learning driven exploration of hydride superconductors at ambient pressure. Computational Materials Today, 10, 100052, (2026)
- M. Keller, H. Wang, F. Bechstedt et al. Optical selection rules in hexagonal Ge polytypes and their lifting by symmetry perturbation. arXiv:2605.10709, 1, xx, (2026)
- T. Cerqueira, H. Wang, S. Botti et al. A non-orthogonal representation for materials based on chemical similarity. npj Computational Materials, 12, 48, (2026)
2025
- P. De Breuck, H. Wang, G. Rignanese et al. Generative AI for crystal structures: a review. npj Computational Materials, 11, 370, (2025)
- K. Gao, W. Cui, T. Cerqueira et al. Enhanced superconductivity in X4H15 compounds via hole-doping at ambient pressure. Advanced Science, 12, e08419, (2025)
- J. Jacobs, C. Ritter, Ke. Xu et al. Structural and electronic tunability of Ruddlesden–Popper oxyfluorides through nickel–copper substitution in La2Ni1−xCuxO2.5F3 (0 ≤ x ≤ 1). Journal of Materials Chemistry A, 13, 32539–32550, (2025)
- T. da Silva, T. Cerqueira, H. Wang et al. High-throughput study of kagome compounds in the AV3Sb5 family. Digital Discovery, 4, 2431–2438, (2025)
- K. Gao, T. Cerqueira, A. Sanna et al. The maximum Tc of conventional superconductors at ambient pressure. Nature Communications, 16, 8253, (2025)
- A. Loew, D. Sun, H. Wang et al. Universal machine learning interatomic potentials are ready for phonons. npj Computational Materials, 11, 178, (2025)
- T. da Silva, T. Cavignac, T. Cerqueira et al. Machine-learning accelerated prediction of two-dimensional conventional superconductors. Materials Horizons, -, -, (2025)
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)
- 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)
- 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)
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)
- J. Schmidt, N. Hoffmann, H. Wang et al. Machine-learning-assisted determination of the global zero-temperature phase diagram of materials. Advanced Materials, 35, 2210788, (2023)
- J. Schmidt, H. Wang, G. Schmidt et al. Machine learning guided high-throughput search of non-oxide garnets. npj Computational Materials, 9, 63, (2023)
2021
- H. Wang, S. Botti, M. Marques. Predicting stable crystalline compounds using chemical similarity. npj Computational Materials, 7, 12, (2021)