activity
20192021
most citedActive Learning A Neural Network Model For Gold Clusters \& Bulk From Sparse First Principles Training Data

30 citations · 78 across the 7 of their papers we have counts for

collaborators

8 papers

cond-mat.mtrl-sci202128 cited

Learning with Delayed Rewards -- A case study on inverse defect design in 2D materials

Suvo Banik, Troy D Loeffler, Rohit Batra +3

Defect dynamics in materials are of central importance to a broad range of technologies from catalysis to energy storage systems to microelectronics. Material functionality depends…

physics.comp-ph202030 cited

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…

cs.LG2020

Combinatorial Black-Box Optimization with Expert Advice

Hamid Dadkhahi, Karthikeyan Shanmugam, Jesus Rios +4

We consider the problem of black-box function optimization over the boolean hypercube. Despite the vast literature on black-box function optimization over continuous domains, not m…

cond-mat.soft20201 cited

Accelerating Copolymer Inverse Design using AI Gaming algorithm

Tarak K Patra, Troy D. Loeffler, Subramanian K R S Sankaranarayanan

There exists a broad class of sequencing problems, for example, in proteins and polymers that can be formulated as a heuristic search algorithm that involve decision making akin to…

cs.CE20202 cited

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…

physics.comp-ph2019

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…