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.
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.