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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)
2025
D. Dangić , Y. Fang, T. Cerqueira et al. Ambient pressure high temperature superconductivity in RbPH₃ facilitated by ionic anharmonicity. Computational Materials Today, 8, 100043, (2025)
J. Hu, W. Ruan, Q. Bai et al. Axial coordination of iron single-atom catalysts on defective graphene for electrocatalytic conversion of Nitric Oxide to Hydroxylamine: a theoretical investigation. Langmuir, 41, 33829–33837, (2025)
P. De Breuck, H. Wang, G. Rignanese et al. Generative AI for crystal structures: a review. npj Computational Materials, 11, 370, (2025)
G. Benedini, A. Loew, M. Hellström et al. Universal machine learning potentials for systems with reduced dimensionality. AI for Science, 1, 025005, (2025)
K. Sharma, A. Loew, H. Wang et al. Accelerating point defect photo-emission calculations with machine learning interatomic potentials. npj Computational Materials, 11, 334, (2025)
X. Fu, X. Guo, P. Shi et al. Control of CO₂ electrocatalysis via modularly customizable graphdiyne. Journal of the American Chemical Society, 147, 42394–42405, (2025)
X. Li, W. Xu, Z. Zhou et al. High-Tc superconductivity above 130 K in cubic MH4 compounds at ambient pressure. arXiv: 2511.04222, 1, 9, (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 La₂Ni_1−xCu_xO_2.5F₃ (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. Xie, Ye. Shen, L. Lin et al. Machine learning-enhanced design of 2D TM₃(HXBHYB)@MOF-based single-atom catalysts for efficient oxygen electrocatalysis. The Journal of Physical Chemistry Letters, 16, 9682, (2025)
Q. Wang, X. Guo, Mo. Lin et al. Protonation and deprotonation of edges in graphene oxide and MXenes as a driving force for actuation in responsive 2D membranes. Nature Communications, 16, 8683, (2025)
K. Gao, T. Cerqueira, A. Sanna et al. The maximum Tc of conventional superconductors at ambient pressure. Nature Communications, 16, 8253, (2025)
W. Wang, X. Guo, Y. Wang et al. Transformation of CO₂ to C₂₊ alcohols by tailoring the oxygen bonding via Fe-based tandem catalyst. Nature Communications, 16, 7265, (2025)
A. Loew, D. Sun, H. Wang et al. Universal machine learning interatomic potentials are ready for phonons. npj Computational Materials, 11, 178, (2025)
J. Deng, Y. Jiang, T. F. T. Cerqueira et al. Theory of Superconductivity in LaRu₃Si₂ and Predictions of New Kagome Flat Band Superconductors. arXiv:2503.20867, 1, 71, (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. Structure prediction and characterization of CuI-based ternary -type transparent conductors. Journal of Materials Chemistry C, 23, 8320-8333, (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)
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)
T. F. 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. 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)
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)