A review commentary arguing that machine-learned interatomic potentials can carry quantum-level accuracy from small simulations to million-atom studies of carbon phase transitions and nucleation.
Phase diagram of carbon at high pressures and temperatures
1 Pith paper cite this work, alongside 177 external citations. Polarity classification is still indexing.
1
Pith paper citing it
177
external citations · OpenAlex
citation-role summary
background 1
citation-polarity summary
fields
physics.comp-ph 1years
2025 1verdicts
UNVERDICTED 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
The transformative capability of quantum-accurate machine learning interatomic potentials
A review commentary arguing that machine-learned interatomic potentials can carry quantum-level accuracy from small simulations to million-atom studies of carbon phase transitions and nucleation.