36 citations · 104 across the 9 of their papers we have counts for
4 papers · 1 filter
Efficient Grand Canonical Global Optimization with On-the-fly-trained Machine-learning Interatomic Potentials
Jon Eunan Quinlivan Dominguez, Mads-Peter Verner Christiansen, Konstantin M. Neyman +2
The characterization of nanostructured materials under reactive environments is challenging due to the complexity of the structural motifs involved and their chemical transformatio…
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…
-model correction of Foundation Model based on the models own understanding
Mads-Peter Verner Christiansen, Bjørk Hammer
Foundation models of interatomic potentials, so called universal potentials, may require fine-tuning or residual corrections when applied to specific subclasses of materials. In th…