53 citations · 312 across the 35 of their papers we have counts for
5 papers · 1 filter
Active Learning A Neural Network Model For Gold Clusters \& Bulk From Sparse First Principles Training Data
Troy D Loeffler, Sukriti Manna, Tarak K Patra +3
Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs d…
Machine Learning for Multi-fidelity Scale Bridging and Dynamical Simulations of Materials
Rohit Batra, Subramanian Sankaranarayanan
Molecular dynamics (MD) is a powerful and popular tool for understanding the dynamical evolution of materials at the nano and mesoscopic scales. There are various flavors of MD ran…
Active Learning the Coarse-Grained Energy Landscape For Water Clusters From Sparse Training Data
Troy D. Loeffler, Tarak K. Patra, Henry Chan +2
ANNs are currently trained by generating large quantities (On the order of or greater) of structural data in hopes that the ANN has adequately sampled the energy landscape…
A coarse-grained deep neural network model for liquid water
Tarak K Patra, Troy D. Loeffler, Henry Chan +3
We introduce a coarse-grained deep neural network model (CG-DNN) for liquid water that utilizes 50 rotational and translational invariant coordinates, and is trained exclusively ag…
Comparing optimization strategies for force field parameterization
Fatih G. Sen, Badri Narayanan, Jeffrey Larson +7
Classical molecular dynamics (MD) simulations enable modeling of materials and examination of microscopic details that are not accessible experimentally. The predictive capability…