Kipton Barros

According to our database, Kipton Barros authored at least 15 papers between 2010 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Projected Hessian Learning: Fast Curvature Supervision for Accurate Machine-Learning Interatomic Potentials.

Austin Rodriguez, Justin S. Smith, Sakib Matin, Nicholas Lubbers, Kipton Barros, Jose L. Mendoza-Cortes

CoRR, March, 2026

2025

Sunny.jl: A Julia Package for Spin Dynamics.

David Dahlbom, Hao Zhang, Cole Miles, Sam Quinn, Alin Niraula, Bhushan Thipe, Matthew Wilson, Sakib Matin, Het Mankad, Steven Hahn, Daniel Pajerowski, Steve Johnston, Zhentao Wang, Harry Lane, Ying Wai Li, Xiaojian Bai, Martin Mourigal, Cristian D. Batista, Kipton Barros

J. Open Source Softw., December, 2025

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, March, 2025

Multi-fidelity learning for interatomic potentials: low-level forces and high-level energies are all you need^*.

Mitchell Messerly, Sakib Matin, Alice E. A. Allen, Benjamin T. Nebgen, Kipton Barros, Justin S. Smith, Nicholas Lubbers, Richard A. Messerly

Mach. Learn. Sci. Technol., 2025

Unveiling Process-Structure Mapping with a Deep Variational Autoencoder.

Avanish Mishra, Brenden William Hamilton, Mashroor Shafat Nitol, Nithin Mathew, Kipton Barros, Timothy C. Germann, Saryu Fensin

Proceedings of the IEEE/CVF International Conference on Computer Vision, ICCV 2025, 2025

2023

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

2021

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

2020

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

2017

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

Proceedings of the 2017 IEEE International Parallel and Distributed Processing Symposium Workshops, 2017

2016

Inferring low-dimensional microstructure representations using convolutional neural networks.

Nicholas Lubbers, Turab Lookman, Kipton Barros

CoRR, 2016

Learning Potential Energy Landscapes using Graph Kernels.

Grégoire Ferré, Terry Haut, Kipton Barros

CoRR, 2016

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

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

2014

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

2010

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