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Found 6 papers, 2 papers with code
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.
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
Machine learning plays an important and growing role in molecular simulation.
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials
Machine learning potentials are an important tool for molecular simulation, but their development is held back by a shortage of high quality datasets to train them on.
NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic
Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations.
Classical Quantum Optimization with Neural Network Quantum States
The classical simulation of quantum systems typically requires exponential resources.
Disordered Systems and Neural Networks Strongly Correlated Electrons Quantum Physics
Weakly-Supervised Deep Learning of Heat Transport via Physics Informed Loss
In typical machine learning tasks and applications, it is necessary to obtain or create large labeled datasets in order to to achieve high performance.
