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洞見 - Molecular simulation
Comprehensive Machine Learning Force Field for Accurate Simulation of Liquid Electrolytes
BAMBOO, a novel machine learning force field framework, achieves state-of-the-art accuracy in predicting key properties of diverse liquid electrolytes, including density, viscosity, and ionic conductivity, through the integration of physics-inspired modeling, ensemble knowledge distillation, and density alignment.
Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations
A general reweighting framework based on anisotropic diffusion maps is provided to construct low-dimensional collective variables directly from enhanced sampling simulation data.
Learning Collective Variables for Protein Folding through Physics-inspired Geodesic Interpolation
Leveraging physics-inspired geodesic interpolation, we propose an effective simulation-free data augmentation strategy to improve the learning of collective variables for enhanced sampling of protein folding.