2 citations · 3 across the 3 of their papers we have counts for
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Coarse-Graining with Equivariant Neural Networks: A Path Towards Accurate and Data-Efficient Models
Timothy D. Loose, Patrick G. Sahrmann, Thomas S. Qu +1
Machine learning has recently entered into the mainstream of coarse-grained (CG) molecular modeling and simulation. While a variety of methods for incorporating deep learning into…
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…
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…