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14 papers · 2 filters
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…
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…
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…
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…
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…
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…