SuperSalt is a MACE-based machine learning potential that predicts thermophysical properties of 11-cation chloride melts with near-DFT accuracy and enables Bayesian optimization of salt compositions.
GPTFF: A high-accuracy out-of-the-box universal AI force field for arbitrary inorganic materials
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
This study introduces a novel AI force field, namely graph-based pre-trained transformer force field (GPTFF), which can simulate arbitrary inorganic systems with good precision and generalizability. Harnessing a large trove of the data and the attention mechanism of transformer algorithms, the model can accurately predict energy, atomic forces, and stress with Mean Absolute Error (MAE) values of 32 meV/atom, 71 meV/{\AA}, and 0.365 GPa, respectively. The dataset used to train the model includes 37.8 million single-point energies, 11.7 billion force pairs, and 340.2 million stresses. We also demonstrated that GPTFF can be universally used to simulate various physical systems, such as crystal structure optimization, phase transition simulations, and mass transport.
citation-role summary
baseline 1
citation-polarity summary
fields
cond-mat.mtrl-sci 1years
2024 1verdicts
CONDITIONAL 1roles
baseline 1polarities
baseline 1representative citing papers
citing papers explorer
-
SuperSalt: Equivariant Neural Network Force Fields for Multicomponent Molten Salts System
SuperSalt is a MACE-based machine learning potential that predicts thermophysical properties of 11-cation chloride melts with near-DFT accuracy and enables Bayesian optimization of salt compositions.