K
Kipton Barros
Researcher at Los Alamos National Laboratory
Publications - 111
Citations - 3676
Kipton Barros is an academic researcher from Los Alamos National Laboratory. The author has contributed to research in topics: Artificial neural network & Computer science. The author has an hindex of 25, co-authored 87 publications receiving 2377 citations. Previous affiliations of Kipton Barros include Northwestern University & Boston University.
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Solving Lattice QCD systems of equations using mixed precision solvers on GPUs
TL;DR: A new mixed precision approach for Krylov solvers using reliable updates is developed which allows for full double precision accuracy while using only single or half precision arithmetic for the bulk of the computation.
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Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning.
Justin S. Smith,Justin S. Smith,Benjamin Nebgen,Roman I. Zubatyuk,Roman I. Zubatyuk,Nicholas Lubbers,Christian Devereux,Kipton Barros,Sergei Tretiak,Olexandr Isayev,Adrian E. Roitberg +10 more
TL;DR: A general-purpose neural network potential is trained that approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions.
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Hierarchical modeling of molecular energies using a deep neural network
TL;DR: HIP-NN achieves the state-of-the-art performance on a dataset of 131k ground state organic molecules and predicts energies with 0.26 kcal/mol mean absolute error.
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Machine Learning Predicts Laboratory Earthquakes
Bertrand Rouet-Leduc,Bertrand Rouet-Leduc,Claudia Hulbert,Nicholas Lubbers,Nicholas Lubbers,Kipton Barros,Colin J. Humphreys,Paul A. Johnson +7 more
TL;DR: In this article, the authors apply machine learning to data sets from shear laboratory experiments, with the goal of identifying hidden signals that precede earthquakes, and infer that this signal originates from continuous grain motions of the fault gouge as the fault blocks displace.
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Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens.
Christian Devereux,Justin S. Smith,Kate K. Davis,Kipton Barros,Roman I. Zubatyuk,Olexandr Isayev,Adrian E. Roitberg +6 more
TL;DR: This work provides an extension of the ANI-1x model that is trained to three additional chemical elements: S, F, and Cl, and is shown to accurately predict molecular energies compared to DFT with a ~106 factor speedup and a negligible slowdown.