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Found 4 papers, 2 papers with code
On the Inclusion of Charge and Spin States in Cartesian Tensor Neural Network Potentials
In this letter, we present an extension to TensorNet, a state-of-the-art equivariant Cartesian tensor neural network potential, allowing it to handle charged molecules and spin states without architectural changes or increased costs.
TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations
Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge.
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
The development of efficient machine learning models for molecular systems representation is becoming crucial in scientific research.
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Geometric Deep Learning for the Assessment of Thrombosis Risk in the Left Atrial Appendage
The assessment of left atrial appendage (LAA) thrombogenesis has experienced major advances with the adoption of patient-specific computational fluid dynamics (CFD) simulations.
