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Our study on learning from MD data to predict solvation free energies and partition coefficients was published recently in J. Chem. Inf. Model. entitled Molecular dynamics fingerprints (MDFP): Machine-learning from MD data to predict free-energy differences.

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Our study on the improvement of the parametrization of the atomistic/coarse-grained interactions was published recently in J. Chem. Phys. entitled Improved accuracy of hybrid atomistic/coarse-grained simulations using reparametrised interactions.

Our RE-EDS method to calculate efficiently free-energy differences was published recently in J. Chem. Phys. entitled Replica-exchange enveloping distribution sampling (RE-EDS): A robust method to estimate multiple free-energy differences from a single simulation.

 
 
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29.04.2017
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