Solving lattice QCD systems of equations using mixed precision solvers on GPUs. - Michael A. Clark; Ronald Babich; Kipton Barros; Richard C. Brower; Claudio Rebbi (Comput. Phys. Commun., 2010)

Spatial adaptive sampling in multiscale simulation. - Bertrand Rouet-Leduc; Kipton Barros; Emmanuel Cieren; Venmugil Elango; Christoph Junghans; Turab Lookman; Jamaludin Mohd-Yusof; Robert S. Pavel; Axel Y. Rivera; Dominic Roehm; Allen L. McPherson; Timothy C. Germann (Comput. Phys. Commun., 2014)

Distributed Database Kriging for Adaptive Sampling (D2KAS). - Dominic Roehm; Robert S. Pavel; Kipton Barros; Bertrand Rouet-Leduc; Allen L. McPherson; Timothy C. Germann; Christoph Junghans (Comput. Phys. Commun., 2015)

Comparison of efficient techniques for the simulation of dielectric objects in electrolytes. - Zecheng Gan; Huanxin Wu; Kipton Barros; Zhenli Xu; Erik Luijten (J. Comput. Phys., 2015)

Learning Potential Energy Landscapes using Graph Kernels. - Grégoire Ferré; Terry Haut; Kipton Barros (CoRR, 2016)

Inferring low-dimensional microstructure representations using convolutional neural networks. - Nicholas Lubbers; Turab Lookman; Kipton Barros (CoRR, 2016)

A Case Study in Computational Caching Microservices for HPC. - John Jenkins; Galen M. Shipman; Jamaludin Mohd-Yusof; Kipton Barros; Philip H. Carns; Robert B. Ross (IPDPS Workshops, 2017)

Automated discovery of a robust interatomic potential for aluminum. - Justin S. Smith; Benjamin T. Nebgen; Nithin Mathew; Jie Chen; Nicholas Lubbers; Leonid Burakovsky; Sergei Tretiak; Hai Ah Nam; Timothy C. Germann; Saryu Fensin; Kipton Barros (CoRR, 2020)

Machine-learning Kondo physics using variational autoencoders. - Cole Miles; Matthew R. Carbone; Erica J. Sturm; Deyu Lu; Andreas Weichselbaum; Kipton Barros; Robert M. Konik (CoRR, 2021)

Uncertainty-driven dynamics for active learning of interatomic potentials. - Maksim Kulichenko; Kipton Barros; Nicholas Lubbers; Ying Wai Li; Richard A. Messerly; Sergei Tretiak; Justin S. Smith; Benjamin T. Nebgen (Nat. Comput. Sci., 2023)

Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials. - Sakib Matin; Emily Shinkle; Yulia Pimonova; Galen T. Craven; Aleksandra Pachalieva; Ying Wai Li; Kipton Barros; Nicholas Lubbers (CoRR, 2025)