2 citations · 3 across the 3 of their papers we have counts for
3 papers
cond-mat.mtrl-sci2025★ 2 cited
Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning
Mariia Radova, Wojciech G. Stark, Connor S. Allen +2
Machine-learned interatomic potentials are revolutionising atomistic materials simulations by providing accurate and scalable predictions within the scope covered by the training d…
cond-mat.mtrl-sci2025
Multi-Phase Dataset for Ti and Ti-6Al-4V
Connor S. Allen, Albert P. Bartók
Titanium and its alloys are technologically important materials that display a rich phase behaviour. In order to enable large-scale, realistic modelling of Ti and its alloys on the…
cond-mat.mtrl-sci2022★ 1 cited
Optimal data generation for machine learned interatomic potentials
Connor Allen, Albert P. Bartók
Machine learning interatomic potentials (MLIPs) are routinely used atomic simulations, but generating databases of atomic configurations used in fitting these models is a laborious…