About to Interatomic Forcefield Parameterization By Active Learning
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Developments
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Active Learning of Fast Bayesian Mapped Gaussian Processes
Daniel Schwalbe Koda: Machine learning for interatomic potentials
Computational Chemistry 2.3 - Force Field Parameters
Interatomic energy in molecular dynamics simulations
Beyond Interatomic Potentials - Further Acceleration of Atomic-Scale SImulations
Reproducible Simulation Workflows and Machine Learning Directed Force Field Development
nanoHUB-U Atoms to Materials L5.4: Reactive Interatomic Potentials
Yuanqing Wang - Parameterization of Extended Force Field using Graph Neural Nets
Force Field Parameterization
Computational Chemistry 2.3 - Force Field Parameters (Old Version)
Using machine learning to improve RNA force fields
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Last Updated: August 16, 2026
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