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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)
- S. Baghaee Ravari, A. Azócar Guzmán, S. Menon et al. Ontology‐aligned structuring and reuse of multimodal materials data and workflows toward automatic reproduction. Advanced Engineering Materials, 28, e202600008, (2026)
- A. Smekhova, A. Kuzmin, G. Muralikrishna et al. Impact of interstitial carbon on local lattice distortions in CoCrFeMnNi high-entropy alloys. Acta Materialia, 319, 122664, (2026)
- S. Noorzayee, S. Mai, S. Menon et al. Recycling of NiTi shape memory alloys: fundamental and technological aspects of a vacuum induction melting processing route. Advanced Engineering Materials, 28, e202503197, (2026)
- L. Medrano Sandonas, D. Balcells, T. Barnowsky et al. Towards sustainable exploration of chemical spaces with machine learning. Digital Discovery, xx, xx, (2026)
- A. Vishwakarma, S. Menon, F. Körmann et al. Foundational machine‐learning interatomic potential for simulating chemically complex Ni‐Based superalloys. Advanced Engineering Materials, 28, e71125, (2026)
- Y. Ikeda, X. Xu, P. Kumar et al. Atomistic simulations of high-entropy alloys: from density functional theory to machine-learning interatomic potentials. Journal of Materials Science, xx, s10853, (2026)
- D. Smirnova, S. Starikov, E. Bitzek. Direct atomistic simulation of dislocation climb in fcc Ni. Physical Review Materials, 10, 083601, (2026)
- S. Starikov, P. Grigorev, D. Smirnova. Efficient and accurate interatomic potential for atomistic study of diffusion and precipitation in Cu-Ag-Co alloys. Physical Review Materials, 10, 083403, (2026)
- A. Azócar Guzmán, H. Luu, S. Menon et al. Toward knowledge‐based workflows: a semantic approach to atomistic simulations for mechanical and thermodynamic properties. Advanced Engineering Materials, 28, e70869, (2026)
- F. Körmann, Y. Ikeda, K. Glazyrin et al. Hydrogen uptake and hydride formation in AlxCoCrFeNi high-entropy alloys: First-principles, universal-potential, and experimental study. Acta Materialia, 313, 122300, (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)
- A. Vesti, D. Music, M. Forti et al. Mechanical stability and fracture behavior of the WOs sigma phase: Effects of composition and lattice-site occupancy. Computational Materials Science, 269, 114699, (2026)
- E. Ibrahim, Y. Lysogorskiy, R. Drautz et al. Water phase diagram from a general-purpose atomic cluster expansion potential. Journal of Chemical Theory and Computation, 22, 4758, (2026)
- S. Brinckmann, S. Menon, R. Röske et al. A lightweight procedural layer for hybrid experimental–computational workflows in materials science. Advanced Engineering Materials, 1, e202503176, (2026)
- M. Forti, A. Malakhova, Y. Lysogorskiy et al. Data-efficient machine-learning of complex Fe–Mo intermetallics using domain knowledge of chemistry and crystallography. npj Computational Materials, 12, 161, (2026)
- P. Kumar, F. Körmann, K. Edalati et al. Hydrogen diffusion in TiCr2H Laves phases: A combined ab initio and machine-learning-potential study. Acta Materialia, 308, 122048, (2026)
- Z. Pei, Y. Gong, P. Singh et al. Can chemical short-range order be transformed into a practical alloy-engineering tool?. Current Opinion in Solid State and Materials Science, 41, 101254, (2026)
- K. Glazyrin, K. Spektor, M. Bykov et al. Synthesis of high-entropy hydride from the cantor alloy (fcc–CoCrFeNiMn) at extreme conditions. Nature Communications, 17, 2622, (2026)
- Y. Lysogorskiy, A. Bochkarev, R. Drautz. Graph atomic cluster expansion for foundational machine learning interatomic potentials. npj Computational Materials, 12, 114, (2026)
- F. Körmann, A. Forslund, Y. Ikeda et al. Impact of thermal excitations on the stabilization of the disordered VCoNi alloy. Physical Review Materials, 10, 023604, (2026)
- S. Starikov, P. Grigorev, S.-H. Lee et al. Angular-dependent interatomic potential for large-scale simulation of bcc and hcp multi-component refractory alloys. Computational Materials Science, 262, 114369, (2026)
- X. Wu, Z. Sun, W. Guo et al. Microstructural origin of the simultaneous enhancements in strength and ductility of a nitrogen-doped high-entropy alloy. Acta Materialia, 304, 121753, (2026)
- S. Menon, A. Azócar Guzmán, O. Waseda et al. tools4RDF: A Python toolkit for working with RDF data. Journal of Open Source Software, 11, 1, (2026)