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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)
- K. H. Hiorth, M. Gutierrez-Amigo, T. Cavignac et al. Ab-initio superfluid weight and superconducting penetration depth. Physical Review B, 1, 12, (2026)
- T. Bispo Da Silva, H. Wang, T. F. T. Cerqueira et al. High-throughput study of electrical conductivity in ordered metals. arXiv:2605.22167, 1, xx, (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)
- H. Li, L. Lin, X. Guo et al. Electronic structure engineering in MXene SACs: unveiling the role of mixed termination for ORR/OER bifunctionality. Small, 22, e14330, (2026)
- H. Lv, F. Guo, J. Li et al. Reverse reaction pathways for efficient CO2–to–formic acid conversion at Cu2O–Bi2O3 interfaces in ionic liquids. ACS Catalysis, 16, 7633–7645, (2026)
- N. Bhatia, O. Krejci, S. Botti et al. MACE4IRmol: An uncertainty-aware foundation model for molecular infrared spectroscopy. arXiv:2508.19118v2, 1, 36, (2026)
- V. Dovale-Farelo, P. Tavadze, M. Marques et al. System-conditioned reparameterization of the SCAN functional for accurate bandgaps: from analytical constraints to machine learning. npj Computational Materials, 12, 162, (2026)
- A. Loew, J. Schmidt, S. Botti et al. Universal machine learning potentials under pressure. Journal of Physics: Materials, 9, 015010, (2026)
- R. A. Mustaf, K. P. Sajilesh, S. Mishra et al. Machine learning-guided discovery of Kagome superconductors YRu3B2 and LuRu3B2. arXiv: 2512. 16945, 1, 15, (2026)
- F. Tran, S. Lehtola, S. Pittalis et al. Semi-local exchange-correlation approximations in density functional theory. arXiv: 2602.1733, 1, 194, (2026)
- P. De Breuck, H. Piracha, G. Rignanese et al. A generative material transformer using Wyckoff representation. npj Computational Materials, 12, 60, (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)
- A. Peng, C. Cai, M. Guo et al. LAMBench: a benchmark for large atomistic models. npj Computational Materials, 12, 62, (2026)