most citedUtilizing Machine Learning to Greatly Expand the Range and Accuracy of Bottom-Up Coarse-Grained Models Through Virtual Particles

1 citations · 1 across the 2 of their papers we have counts for

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2 papers

physics.chem-ph2022★ 1 cited

Utilizing Machine Learning to Greatly Expand the Range and Accuracy of Bottom-Up Coarse-Grained Models Through Virtual Particles

Patrick G. Sahrmann, Timothy D. Loose, Aleksander E. P. Durumeric +1

Coarse-grained (CG) models parameterized using atomistic reference data, i.e., 'bottom up' CG models, have proven useful in the study of biomolecules and other soft matter. However…

physics.chem-ph2022

Centroid Molecular Dynamics Can Be Greatly Accelerated Through Neural Network Learned Centroid Forces Derived from Path Integral Molecular Dynamics

Timothy D. Loose, Patrick G. Sahrmann, Gregory A. Voth

For nearly the past 30 years, Centroid Molecular Dynamics (CMD) has proven to be a viable classical-like phase space formulation for the calculation of quantum dynamical properties…