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Found 4 papers, 2 papers with code
Accurate machine learning force fields via experimental and simulation data fusion
Machine Learning (ML)-based force fields are attracting ever-increasing interest due to their capacity to span spatiotemporal scales of classical interatomic potentials at quantum-level accuracy.
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls
Neural network (NN) potentials promise highly accurate molecular dynamics (MD) simulations within the computational complexity of classical MD force fields.
Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting
In molecular dynamics (MD), neural network (NN) potentials trained bottom-up on quantum mechanical data have seen tremendous success recently.
Accelerated Simulations of Molecular Systems through Learning of their Effective Dynamics
We believe that the proposed framework provides a dramatic increase to simulation capabilities and opens new horizons for the effective modeling of complex molecular systems.
