71 citations · 311 across the 15 of their papers we have counts for
15 papers
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…
BLAST: Bridging Length/time scales via Atomistic Simulation Toolkit
Henry Chan, Badri Narayanan, Mathew Cherukara +4
The ever-increasing power of supercomputers coupled with highly scalable simulation codes have made molecular dynamics an indispensable tool in applications ranging from predictive…
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…
Machine Learning Prediction of Accurate Atomization Energies of Organic Molecules from Low-Fidelity Quantum Chemical Calculations
Logan Ward, Ben Blaiszik, Ian Foster +3
Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the tradeoff between accuracy and computational cost. Here, we ass…
Graphene Overcoats for Ultra-High Storage Density Magnetic Media
N. Dwivedi, A. K. Ott, K. Sasikumar +10
Hard disk drives (HDDs) are used as secondary storage in a number of digital electronic devices owing to low cost (0.1$/GB at 2016 prices) and large data storage capacity (10TB…
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…