14 citations · 18 across the 3 of their papers we have counts for
3 papers
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…
RAFFLE: Active learning accelerated interface structure prediction
Ned Thaddeus Taylor, Joe Pitfield, Francis Huw Davies +1
Interfaces between materials play a crucial role in the performance of most devices. However, predicting the structure of a material interface is computationally demanding due to t…
Augmentation of Universal Potentials for Broad Applications
Joe Pitfield, Florian Brix, Zeyuan Tang +4
Universal potentials open the door for DFT level calculations at a fraction of their cost. We find that for application to systems outside the scope of its training data, CHGNet\ci…