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20022026
most citedThe Gaia mission

7.1k citations

Showing 2025 · cond-mat.mtrl-sciShow all

14 papers · 2 filters

cond-mat.mtrl-sci20251 cited

How accurate are foundational machine learning interatomic potentials for heterogeneous catalysis?

Luuk H. E. Kempen, Raffaele Cheula, Mie Andersen

Foundational machine learning interatomic potentials (MLIPs) are being developed at a rapid pace, promising closer and closer approximation to ab initio accuracy. This unlocks the…

cond-mat.mtrl-sci2025

Resonantly enhanced photoemission from topological surface states in MnBiTe

Paulina Majchrzak, Alfred J. H. Jones, Klara Volckaert +5

The dispersion of topological surface bands in MnBiTe-based magnetic topological insulator heterostructures is strongly affected by band hybridization and is spatially inho…

cond-mat.mtrl-sci2025

The crystalline properties of silica biomorphs vary within and between morphologies

Moritz P. K. Frewein, Britta Maier, Moritz L. Stammer +8

Silica-witherite biomorphs are a class of emergent materials, i.e. composite microstructures made of nanometric barium carbonate surrounded by amorphous silica. They form via co-pr…

cond-mat.mtrl-sci2025

Intrinsic physical properties of flexible van der Waals semiconductor InSe

Jacob Svane, Kim-Khuong Huynh, Yong P. Chen +1

InSe is a van der Waals semiconductor in which mechanical flexibility, high electronic mobility, and non-trivial electronic structures converge, making it an attractive platform fo…

cond-mat.mtrl-sci20251 cited

Active Δ-learning with universal potentials for global structure optimization

Joe Pitfield, Mads-Peter Verner Christiansen, Bjørk Hammer

Universal machine learning interatomic potentials (uMLIPs) have recently been formulated and shown to generalize well. When applied out-of-sample, further data collection for impro…

cond-mat.mtrl-sci2025

Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence

Mads-Peter Verner Christiansen, Bjørk Hammer

Machine learning interatomic potentials have become an indispensable tool for materials science, enabling the study of larger systems and longer timescales. State-of-the-art models…