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Research Center Future Energy Materials and Systems

Homepage: rc-fems.de
ROR: 01wk5db87
OpenAlex ID: I4405274527
Improving machine-learning models in materials science through large datasets
2024 · Jonathan Schmidt, Tiago F.T. Cerqueira, et al. · Materials Today Physics · 162
Universal machine learning interatomic potentials are ready for phonons
2025 · Antoine Loew, Dewen Sun, et al. · npj Computational Materials · 128
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Prediction of ambient pressure conventional superconductivity above 80 K in hydride compounds
2024 · Antonio Sanna, Tiago F. T. Cerqueira, et al. · npj Computational Materials · 120
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Searching Materials Space for Hydride Superconductors at Ambient Pressure
2024 · Tiago F. T. Cerqueira, Yue‐Wen Fang, et al. · Advanced Functional Materials · 95
Sampling the Materials Space for Conventional Superconducting Compounds
2023 · Tiago F. T. Cerqueira, Antonio Sanna, et al. · Advanced Materials · 69
Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
2024 · Matthew L. Evans, Johan Bergsma, et al. · Digital Discovery · 65
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Topological grain boundary segregation transitions
2024 · Vivek Devulapalli, Enze Chen, et al. · Science · 61
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Generative AI for crystal structures: a review
2025 · Pierre-Paul De Breuck, Hai-Chen Wang, et al. · npj Computational Materials · 56
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Unraveling spin density wave order in layered nickelates La 3 Ni 2 O 7 and La 2 PrNi 2 O 7 via neutron diffraction
2026 · Igor Plokhikh, Thomas J. Hicken, et al. · Physical Review Research · 50
Prediction of high- Tc superconductivity in ternary actinium beryllium hydrides at low pressure
2024 · Kun Gao, Wenwen Cui, et al. · Physical review. B./Physical review. B · 48
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Direct bandgap quantum wells in hexagonal Silicon Germanium
2024 · Wouter H. J. Peeters, Victor T. van Lange, et al. · Nature Communications · 35
The maximum Tc of conventional superconductors at ambient pressure
2025 · Kun Gao, Tiago F. T. Cerqueira, et al. · Nature Communications · 32
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AI-Driven expansion and application of the Alexandria database
2026 · Théo Cavignac, Jonathan Schmidt, et al. · Journal of Physics Materials · 28
Reproducibility of density functional approximations: How new functionals should be reported
2023 · Susi Lehtola, Miguel A. L. Marques · The Journal of Chemical Physics · 28
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A generative material transformer using Wyckoff representation
2026 · Pierre-Paul De Breuck, Hashim A. Piracha, et al. · npj Computational Materials · 26
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Grain boundary diffusion and segregation of Cr in Ni Σ11(1̄13)[110] bicrystals: Decoding the role of grain boundary defects
2024 · Shraddha V. Sevlikar, G. Mohan Muralikrishna, et al. · Acta Materialia · 24
A modern perspective on antiferroelectrics
2026 · Gustau Catalan, Alexei Gruverman, et al. · Nature Materials · 23
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Ubiquitous Order‐Disorder Transition in the Mn Antisite Sublattice of the (MnBi 2 Te 4 )(Bi 2 Te 3 ) n Magnetic Topological Insulators
2024 · Manaswini Sahoo, Ifeanyi John Onuorah, et al. · Advanced Science · 22
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A Unifying Perspective of Common Motifs That Occur across Disparate Classes of Materials Harboring Displacive Phase Transitions
2023 · Anna Grünebohm, Andreas Hütten, et al. · Advanced Energy Materials · 20
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Self-formation of compositionally complex surface oxides on high entropy alloys observed by accelerated atom probe tomography: a route to sustainable catalysts
2024 · Valerie Strotkötter, Yujiao Li, et al. · Materials Horizons · 17
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Accelerating point defect photo-emission calculations with machine learning interatomic potentials
2025 · Kartikeya Sharma, Antoine Loew, et al. · npj Computational Materials · 16
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Hierarchy of defects in near- Σ 5 tilt grain boundaries in copper studied by length-scale bridging electron microscopy
2025 · Hui Ding, Anoosheh Akbari, et al. · Acta Materialia · 16
Low angle grain boundaries and re-segregation in single crystal Ni-base superalloys
2025 · Felicitas Scholz, Alireza B. Parsa, et al. · Journal of Alloys and Compounds · 16
Ambient pressure high temperature superconductivity in RbPH3 facilitated by ionic anharmonicity
2025 · Đorđe Dangić, Yue-Wen Fang, et al. · Computational Materials Today · 15
Machine-learning accelerated prediction of two-dimensional conventional superconductors
2025 · Thalis H. B. da Silva, Théo Cavignac, et al. · Materials Horizons · 11
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Works count: 154
Citation count: 3,336
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