Hi, @LatinXChem! This is my work with @thorvanheesch & @BerndEnsing on data mining & path-based enhanced sampling. Our goal is to go "From high-dimensional stable-state data to interpretable transition pathways and free-energy profiles" #LatinXChem #LatinXChemTheo #Theo154 (1/7).
If you& #39;re curious about #DiSSCoVa, you can watch this one-minute video from the recent @ai4science_lab workshop: https://www.youtube.com/watch?v=KI3BsW7234A">https://www.youtube.com/watch... (2/7)
Here, you can see our favorite didactic animation of #pathmetadynamics in action: https://youtu.be/YRZ3zOO5e_I .">https://youtu.be/YRZ3zOO5e... Notice how the method adapts to find the minimum free energy path! (3/7)
And here, you can see the multiple-path version: https://youtu.be/RNjRAOmQEcw .">https://youtu.be/RNjRAOmQE... Notice how both paths are captured simultaneously! The green path has an slightly higher barrier, but could still compete with the purple one (4/7).
And talking about starting your own #pathmetadynamics runs, here& #39;s our @PlumedN contribution with a ready-to-run example: https://www.plumed-nest.org/eggs/19/033/ .">https://www.plumed-nest.org/eggs/19/0... You can use it with any MD package supported by @plumed_org! (6/7)
You can follow @apdealbao.
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