General Lab Information

Publications

2024

  1. Di Meglio, A. & Yoo, S. (2024). Quantum Computing for High-Energy Physics: State of the Art and Challenges. PRX Quantum, 5(3), Article 037001 https://dx.doi.org/10.1103/prxquantum.5.037001
  2. Diefenthaler, M. & Lin, M. (2024). AI-Assisted Detector Design for the EIC (AID(2)E). Journal of Instrumentation https://dx.doi.org/10.1088/1748-0221/19/07/C07001
  3. Isenberg, N. & Urban, N. (2024). Identifying Bayesian optimal experiments for uncertain biochemical pathway models. Scientific Reports https://dx.doi.org/10.1038/s41598-024-65196-w
  4. Li, Z. & Flynn, T. (2024). Highly sensitive 2D X-ray absorption spectroscopy via physics informed machine learning. npj Computational Materials, 10(1), Article 128 https://dx.doi.org/10.1038/s41524-024-01313-7
  5. Lopez-Marrero, V. (2024). Density estimation via measure transport: Outlook for applications in the biological sciences. Statistical Analysis and Data Mining: The ASA Data Science Journal, 17(3) https://dx.doi.org/10.1002/sam.11687
  6. Yu, X. (2024). INSURE: An Information Theory iNspired diSentanglement and pURification modEl for Domain Generalization. IEEE Transactions on Image Processing, 33, 3508-3519 https://dx.doi.org/10.1109/tip.2024.3404241
  7. Luo, X. (2024). Pathway-based analyses of gene expression profiles at low doses of ionizing radiation. Frontiers in Bioinformatics, 4 https://dx.doi.org/10.3389/fbinf.2024.1280971
  8. Atif, M., López-Marrero, V., Zhang, T., Sharfuddin, A. A. M., Yu, K., Yang, J., Yang, F., Ladeinde, F., Liu, Y., Lin, M., Li, L. (2024). Towards accelerating particle‐resolved direct numerical simulation with neural operators. Statistical Analysis and Data Mining: The ASA Data Science Journal, 17(3) https://dx.doi.org/10.1002/sam.11690
  9. Wu, Longlong; Yoo, Shinjae; Chu, Yong S.; Huang, Xiaojing; Robinson, Ian K. (2024). Dose-efficient Automatic Differentiation for Ptychographic Reconstruction. Optica https://dx.doi.org/10.1364/OPTICA.522380
  10. Grosjean, N. & Chopra, K. (2024). A hemoprotein with a zinc-mirror heme site ties heme availability to carbon metabolism in cyanobacteria. Nature Communications, 15(1), Article 3167 https://dx.doi.org/10.1038/s41467-024-47486-z
  11. Wang, H. (2024). Exploring Robust Features for Improving Adversarial Robustness. IEEE Transactions on Cybernetics, 1-11 https://dx.doi.org/10.1109/tcyb.2024.3380437
  12. Cao, C. (2024). Atomic insights into the oxidative degradation mechanisms of sulfide solid electrolytes. Cell Reports Physical Science, Article 101909 https://dx.doi.org/10.1016/j.xcrp.2024.101909
  13. Carbone, M. (2024). Accurate, Uncertainty-Aware Classification of Molecular Chemical Motifs from Multimodal X-ray Absorption Spectroscopy. The Journal of Physical Chemistry A, 128(10), 1948-1957 https://dx.doi.org/10.1021/acs.jpca.3c06910
  14. Baker, J. & Yu, K. (2024). Parallel hybrid quantum-classical machine learning for kernelized time-series classification. Quantum Machine Intelligence, 6(1), Article 18 https://dx.doi.org/10.1007/s42484-024-00149-0
  15. He, S. & Yoon, B. (2024). Entropy removal of medical diagnostics. Scientific Reports https://dx.doi.org/10.1038/s41598-024-51268-4
  16. Shastry, T. & Carbone, M. (2024). Machine learning-based discovery of molecular descriptors that control polymer gas permeation. Journal of Membrane Science, 697, 122563 https://dx.doi.org/10.1016/j.memsci.2024.122563
  17. Zhang, T. & Lin, M. (2024). Emulator of PR-DNS: Accelerating Dynamical Fields With Neural Operators in Particle-Resolved Direct Numerical Simulation. Journal of Advances in Modeling Earth Systems (JAMES) https://dx.doi.org/10.1029/2023MS003898
  18. Bao, N. (2024). Holographic entanglement distillation from the surface state correspondence. Journal of High Energy Physics, 2024(1), Article 91 https://dx.doi.org/10.1007/jhep01(2024)091
  19. Goswami, S. & Carbone, M. (2024). Physically interpretable approximations of many-body spectral functions. Physical Review E, 109(1), Article 015302 https://dx.doi.org/10.1103/physreve.109.015302
  20. Zhang, K. & Yu, K. (2024). Optimal Realization of Yang-Baxter Gate on Quantum Computers. Advanced Quantum Technologies https://dx.doi.org/10.1002/qute.202300345

2023

  1. Park, H. & Yang, Z. (2023). A bacterial sensor taxonomy across earth ecosystems for machine learning applications. mSystems https://dx.doi.org/10.1128/msystems.00026-23
  2. Blum, T. & Kelly, C. (2023). ΔI=3/2 and ΔI=1/2 channels of K→ππ decay at the physical point with periodic boundary conditions. Physical Review D, 108(9), Article 094517 https://dx.doi.org/10.1103/physrevd.108.094517
  3. Yoon, B. (2023). Accelerating scientific discoveries through data-driven innovations. Patterns, 4(11), Article 100876 https://dx.doi.org/10.1016/j.patter.2023.100876
  4. Yoon, B. (2023). Optimal decision-making in high-throughput virtual screening pipelines. Patters, 4(11), Article 100875 https://dx.doi.org/10.1016/j.patter.2023.100875
  5. Erdős, P. & Kharel, S. (2023). Degree-preserving graph dynamics: a versatile process to construct random networks. Journal of Complex Networks, 11(6), Article s00208-023-02574-1 https://dx.doi.org/10.1093/comnet/cnad046
  6. Abrahamsen, N. & Bao, N. (2023). Entanglement area law for one-dimensional gauge theories and bosonic systems. Physical Review A, 108(4), Article 042422 https://dx.doi.org/10.1103/physreva.108.042422
  7. Carbone, M. (2023). The Generalized Green's function Cluster Expansion: APython package for simulating polarons. Journal of Open Source Software, 8(90), 5115 https://dx.doi.org/10.21105/joss.05115
  8. Sun, K. & Cao, C. (2023). Degradation of Lithium Iron Phosphate Sulfide Solid-State Batteries by Conductive Interfaces. The Journal of Physical Chemistry C https://dx.doi.org/10.1021/acs.jpcc.3c05039
  9. Johnstone, P. & Yoo, S. (2023). Stochastic projective splitting. Computational Optimization and Applications https://dx.doi.org/10.1007/s10589-023-00528-6
  10. Zhen, Y. & Yoo, S. (2023). Structuring Nutrient Yields throughout Mississippi/Atchafalaya River Basin Using Machine Learning Approaches. Environments, 10(9), 162 https://dx.doi.org/10.3390/environments10090162
  11. Luo, X. (2023). Optimal sensor placement for reconstructing wind pressure field around buildings using compressed sensing. Journal of Building Engineering, 75, Article 106855 https://dx.doi.org/10.1016/j.jobe.2023.106855
  12. Park, D. & Yoo, S. (2023). Overestimated prediction using polygenic prediction derived from summary statistics. BMC Genomic Data, 24(1), Article 52 https://dx.doi.org/10.1186/s12863-023-01151-4
  13. Pouchard, L. (2023). A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows. Digital Discovery https://dx.doi.org/10.1039/D3DD00094J
  14. Aghor, P. & Atif, M. (2023). Effect of outer-cylinder rotation on the radially heated Taylor-Couette flow. Physics of Fluids, 35(9) https://dx.doi.org/10.1063/5.0160816
  15. Yoon, B. & Pouchard, L. (2023). A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows. Digital Discovery, (Issue 5, 2023) https://dx.doi.org/10.1039/D3DD00094J
  16. Kwon, H. & Lu, D. (2023). Harnessing Neural Networks for Elucidating X-ray Absorption Structure-Spectrum Relationships in Amorphous Carbon. The Journal of Physical Chemistry C, 127(33), 16473-16484 https://dx.doi.org/10.1021/acs.jpcc.3c02029
  17. Carbone, M. (2023). Lightshow: a Python package for generating computational x-ray absorption spectroscopy input files. Journal of Open Source Software, 8(87), 5182 https://dx.doi.org/10.21105/joss.05182
  18. Zheng, S. & Lin, Y. (2023). Graphic contrastive learning analyses of discontinuous molecular dynamics simulations: Study of protein folding upon adsorption. Applied Physics Letters, 122(25) https://dx.doi.org/10.1063/5.0157933
  19. Guo, H. & Carbone, M. (2023). Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes. Scientific Data, 10(1), Article 349 https://dx.doi.org/10.1038/s41597-023-02262-4
  20. Luo, X. (2023). A machine learning-based characterization framework for parametric representation of liquid sloshing. Results in Engineering, 18, Article 101148 https://dx.doi.org/10.1016/j.rineng.2023.101148
  21. Sun, H. & Lin, Y. (2023). Coarse-to-fine Task-driven Inpainting for Geoscience Images. IEEE Transactions on Circuits and Systems for Video Technology https://dx.doi.org/10.1109/TCSVT.2023.3276719
  22. Liang, Z. & Qu, X. (2023). Decoding structure-spectrum relationships with physically organized latent spaces. Physical Review Materials, 7(5), Article 053802 https://dx.doi.org/10.1103/physrevmaterials.7.053802
  23. Jackson Lee, Matthew R. Carbone, and Weiguo Yin (2023). Machine learning the spectral function of a hole in a quantum antiferromagnet. Physical Review B, 107(20), Article 205132 https://dx.doi.org/10.1103/PhysRevB.107.205132
  24. He, S. & Yoon, B. (2023). Network Analysis of Academic Medical Center Websites in the United States. Scientific Data, 10, Article 245 (2023) https://dx.doi.org/10.1038/s41597-023-02104-3
  25. Sewell, T. & Bao, N. (2023). Variational quantum simulation of critical Ising model with symmetry averaging. Physical Review A, 107(4), Article 042620 https://dx.doi.org/10.1103/PhysRevA.107.042620
  26. Soto, C. (2023). A Better Method to Calculate Fuel Burnup in Pebble Bed Reactors Using Machine Learning. Nuclear Technology https://www.osti.gov/biblio/1971837
  27. Liu, X. & Lin, Y. (2023). Transferable Adversarial Attack on 3D Object Tracking in Point Cloud. https://dx.doi.org/10.1007/978-3-031-27818-1_37
  28. Qian, X. (2023). Sensitivity analysis of genome-scale metabolic flux prediction. Journal of Computational Biology https://dx.doi.org/10.1089/cmb.2022.0368
  29. Bao, N. (2023). Reconstruction wedges in AdS/CFT with boundary fractallike structures. Physical Review D, 107(6), Article 066014 https://dx.doi.org/10.1103/physrevd.107.066014
  30. Carbone, M. (2023). Uncertainty-aware predictions of molecular x-ray absorption spectra using neural network ensembles. Physical Review Research, 5, Article 013180 https://dx.doi.org/10.1103/PhysRevResearch.5.013180
  31. Watkins, W. & Yoo, S. (2023). Quantum machine learning with differential privacy. Scientific Reports, 13(1), Article 2453 https://dx.doi.org/10.1038/s41598-022-24082-z
  32. Erdős, P. & Kharel, S. (2023). The sequence of prime gaps is graphic. Mathematische Annalen, Article s00208-023-02574-1 https://dx.doi.org/10.1007/s00208-023-02574-1
  33. Park, G. (2023). Quantum multi-programming for Grover's search. Quantum Information Processing, 22(1), 22-54 https://dx.doi.org/10.1007/s11128-022-03793-2
  34. Titov, M. (2023). RADICAL-Pilot and PMIx/PRRTE: Executing heterogeneous workloads at large scale on partitioned HPC resources. Lecture Notes in Computer Science https://dx.doi.org/10.1007/978-3-031-22698-4_5

2022

  1. Ramesh, S. & Titov, M. (2022). The Ghost of Performance Reproducibility Past. Proceedings - 2022 IEEE 18th International Conference on e-Science, eScience 2022 https://dx.doi.org/10.1109/eScience55777.2022.00091
  2. Bao, N. (2022). Code properties of the holographic Sierpinski triangle. Physical Review D, 106(12), Article 126006 https://dx.doi.org/10.1103/physrevd.106.126006
  3. Yusuf, M. & Cao, C. (2022). Simultaneous neutron and X-ray tomography for visualization of graphite electrode degradation in fast-charged lithium-ion batteries. Cell Reports Physical Science, 3(11), Article 101145 https://dx.doi.org/10.1016/j.xcrp.2022.101145
  4. Carbone, M. (2022). When not to use machine learning: a perspective on potential and limitations. MRS Bulletin https://www.osti.gov/biblio/1895069
  5. Cao, C. (2022). Multimodal quantification of degradation pathways during extreme fast charging of lithium-ion batteries. Journal of Materials Chemistry A, 10(44), 23927-23939 https://dx.doi.org/10.1039/D2TA05887A
  6. Pouchard, L. (2022). Challenges for implementing Fair Digital Objects with High Performance Workflows. Research Ideas and Outcomes, 8 https://dx.doi.org/10.3897/rio.8.e94835
  7. Luo, X. (2022). A Bayesian Deep Learning Approach to Near-Term Climate Prediction. Journal of Advances in Modeling Earth Systems https://dx.doi.org/10.1029/2022MS003058
  8. Carbone, M. (2022). Competition between energy- and entropy-driven activation in glasses. Physical Review E, 106, Article 024603 https://www.osti.gov/biblio/1880769
  9. Yu, K. (2022). SPACE: 3D parallel solvers for Vlasov-Maxwell and Vlasov-Poisson equations for relativistic plasmas with atomic transformations. Computer Physics Communications, 277, Article 108396 https://dx.doi.org/10.1016/j.cpc.2022.108396
  10. Siddiqui Matekole, E. & Lin, M. (2022). Methods and Results for Quantum Pulse Control on Superconducting Systems. Ieee https://dx.doi.org/10.1109/IPDPSW55747.2022.00102
  11. Miryala, S. & Deptuch, G. (2022). Design and Challenges of Edge Computing ASICs on Front-End Electronics. Proceedings Of the Twenty Third International Symposium On Quality Electronic Design (Isqed 2022) https://dx.doi.org/10.1109/ISQED54688.2022.9806248
  12. Bao, N. (2022). Topological link models of multipartite entanglement. Quantum https://dx.doi.org/10.22331/q-2022-06-20-741
  13. Li, L. (2022). SimNet: Accurate and High-Performance Computer Architecture Simulation using Deep Learning. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 6(2), 1-24 https://www.osti.gov/biblio/1889629
  14. Maddouri, O. & Yoon, B. (2022). Synthetic data for design and evaluation of binary classifiers in the context of Bayesian transfer learning. Data In Brief, 42 https://dx.doi.org/10.1016/j.dib.2022.108113
  15. Cao, C. (2022). Conformal Pressure and Fast-Charging Li-ion Batteries. Journal of The Electrochemical Society https://www.osti.gov/biblio/1863095
  16. Maddouri, O. & Yoon, B. (2022). Robust importance sampling for error estimation in the context of optimal Bayesian transfer learning. Patterns, 3(3), Article 100428 https://dx.doi.org/10.1016/j.patter.2021.100428
  17. Mishra, A. & Soto, C. (2022). COMPOFF: A Compiler Cost model using Machine Learning to predict the Cost of OpenMP Offloading. The 12th International Workshop on Accelerators and Hybrid Emerging Systems (AsHES) https://www.osti.gov/biblio/1863881
  18. Lin, M. (2022). Evaluation of Portable Programming Models to Accelerate LArTPC Detector Simulations. Journal of Physics: Conference Series https://dx.doi.org/10.1088/1742-6596/2438/1/012036
  19. Lian, R., Huang, B., Wang, L., Liu, Q., Lin, Y., & Ling, H. (2022). End-to-end orientation estimation from 2D cryo-EM 2 images. Acta Crystallographica Section D: Structural Biology, 78(2), 174-186 https://dx.doi.org/10.1107/s2059798321011761
  20. Maddouri, O. & Yoon, B. (2022). Deep graph representations embed network information for robust disease marker identification. Bioinformatics, 38(4), 1075-1086 https://www.osti.gov/biblio/1855099
  21. Chen, S. (2022). Variational quantum reinforcement learning via evolutionary optimization. IOP Science, 3(1) https://www.osti.gov/biblio/1856770
  22. Wu, S. & Yoo, S. (2022). Challenges and opportunities in quantum machine learning for high-energy physics. Nature Reviews Physics, 4(3), 143-144 https://dx.doi.org/10.1038/s42254-022-00425-7
  23. Bao, N. (2022). Magic State Distillation from Entangled States. Physical Review A, 105(2), Article 22602 https://dx.doi.org/10.1103/PhysRevA.105.022602
  24. Chen, S. (2022). Quantum Convolutional Neural Networks for High Energy Physics Data Analysis. Physical Review Research https://www.osti.gov/biblio/1844565
  25. Maddouri, O. & Yoon, B. (2022). Robust importance sampling for error estimation in the context of optimal Bayesian transfer learning. Patterns https://www.osti.gov/biblio/1878301
  26. Wu, S. & Yoo, S. (2022). Challenges and opportunities in quantum machine learning for high-energy physics. Nature Reviews Physics, 4(3 ) https://www.osti.gov/biblio/1841105
  27. Bao, N. (2022). Holevo information of black hole mesostates. American Physical Society, 105(2) https://dx.doi.org/10.1103/PhysRevD.105.026010

2021

  1. Zhao, G. & Alexander, F. (2021). Efficient active learning for Gaussian process classification by error reduction. Advances in Neural Information Processing Systems, 34 https://www.osti.gov/biblio/1842011
  2. Niu, P. & Qian, X. (2021). TRIMER: Transcription Regulation Integrated with MEtabolic Regulation. iScience, 24(11), Article 103218 https://www.osti.gov/biblio/1839206
  3. Clyde, A. & Tan, L. (2021). High-Throughput Virtual Screening and Validation of a SARS-CoV‑2 Main Protease Noncovalent Inhibitor. Journal of Chemical Engineering https://www.osti.gov/biblio/1841104
  4. Al-Saadi, A. & Titov, M. (2021). ExaWorks: Workflows for Exascale. Proceedings Of 16Th Workshop On Workflows In Support Of Large-Scale Science (Works21), 50-57 https://dx.doi.org/10.1109/WORKS54523.2021.00012
  5. Al-Saadi, A. & Titov, M. (2021). ExaWorks: Workflows for Exascale. Proceedings Of 16Th Workshop On Workflows In Support Of Large-Scale Science (Works21), 50-57 https://dx.doi.org/10.1109/WORKS54523.2021.00012
  6. Carbone, M. (2021). Bond-Peierls polaron: Moderate mass enhancement and current-carrying ground state. Physical Review B Letters, 104, Article L140307 https://www.osti.gov/biblio/1835113
  7. Pascuzzi, V. (2021). Computationally Efficient Zero Noise Extrapolation for Quantum Gate Error Mitigation. https://arxiv.org/abs/2110.13338 - PRA https://www.osti.gov/biblio/1839207
  8. Pascuzzi, V. (2021). Computationally Efficient Zero Noise Extrapolation for Quantum Gate Error Mitigation. Physics Review A https://www.osti.gov/biblio/1855088
  9. Kale, V. (2021). OpenMP Application Experiences: Porting to Accelerated Nodes. Science Direct https://www.osti.gov/biblio/1829281
  10. Bao, N. (2021). Black hole cannibalism. International Journal Of Modern Physics D, 30(14), Article 2142019 https://dx.doi.org/10.1142/S0218271821420190
  11. Bhati, A. & Tan, L. (2021). Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers. Royal Society Publishing https://www.osti.gov/biblio/1841103
  12. Woo, H. & Tan, L. (2021). Optimal Decision Making in High-Throughput Virtual Screening Pipelines. arXiv:2109.11683, Sep. 2021. https://www.osti.gov/biblio/1841102
  13. Chen, S. (2021). An end-to-end trainable hybrid classical-quantum classifier. Machine Learning: Science and Technology https://www.osti.gov/biblio/1829278
  14. Wu, S. & Chen, S. (2021). Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC. Phys. Rev. Research https://www.osti.gov/biblio/1829279
  15. Bhati, A. & Jha, S. (2021). Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers. royalsocietypublishing.org/journal/rsfs https://www.osti.gov/biblio/1830193
  16. Plale, B. & Pouchard, L. (2021). Reproducibility Practice in High Performance Computing: Community Survey Results. Autherea 529285 https://dx.doi.org/10.1109/mcse.2021.3096678
  17. Bao, N. (2021). Entanglement wedge cross section inequalities from replicated geometries. Journal Of High Energy Physics, (7), Article 113 https://dx.doi.org/10.1007/JHEP07(2021)113
  18. Nghiem, N. & Chen, S. (2021). A Unified Framework for Quantum Classification. Physical Review Research, 3(3) https://www.osti.gov/biblio/1818933
  19. Bao, N. (2021). Microstate distinguishability, quantum complexity, and the eigenstate thermalization hypothesis. Classical and Quantum Gravity, 38(15), Article 154004 https://dx.doi.org/10.1088/1361-6382/ac0e17
  20. Wu, S. & Chen, S. (2021). Application of Quantum Machine Learning using the Quantum Variational Classifier Method to High Energy Physics Analysis at the LHC on IBM Quantum Computer Simulator and Hardware with 10 qubits. Journal of Physics G: Nuclear and Particle Physics https://www.osti.gov/biblio/1829277
  21. Zhou, T. & Lin, Y. (2021). Point Adversarial Self-Mining: A Simple Method for Facial Expression Recognition. IEEE Transactions on Cybernetics https://dx.doi.org/10.1109/TCYB.2021.3085744
  22. Berdahi, M. & Urban, N. (2021). Statistical emulation of a perturbed basal melt ensemble of an ice sheet model to better quantify Antarctic sea level rise uncertainties. The Cryosphere, 15(6), 2683-2699 https://www.osti.gov/biblio/1805256
  23. Dong, Z. (2021). Porting HEP Parameterized Calorimeter Simulation Code to GPUs. Frontiers in Big Data , 2, Article 2103.14737 https://www.osti.gov/biblio/1812494
  24. Woo, H. & Yoon, B. (2021). MONACO: accurate biological network alignment through optimal neighborhood matching between focal nodes. Bioinformatics, Volume 37(Issue 10), 1401-1410 https://dx.doi.org/10.1093/bioinformatics/btaa962
  25. Flynn, T. (2021). A persistent adjoint method with dynamic time-scaling and an application to mass action kinetics. Numerical Algorithms https://www.osti.gov/biblio/1798479
  26. Chen, S. (2021). Federated Quantum Machine Learning. Entropy https://www.osti.gov/biblio/1788053
  27. Casalino, L. & Jha, S. (2021). AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics. The International Journal of High Performance Computing Applications https://www.osti.gov/biblio/1788054
  28. Luo, X. (2021). Dynamic mode decomposition of random pressure fields over bluff bodies. Journal of Engineering Mechanics , 147(4) https://dx.doi.org/10.1061/(ASCE)EM.1943-7889.0001904
  29. Pandey, S. & Li, L. (2021). TRUST: Triangle Counting Reloaded on GPUs. IEEE Transactions on Parallel and Distributed Systems, 32(11), 2646-2660 https://dx.doi.org/10.1109/TPDS.2021.3064892
  30. Merzky, A. & Jha, S. (2021). Design and Performance Characterization of RADICAL-Pilot on Leadership-class Platforms. arXiv:2103.00091 https://www.osti.gov/biblio/1830194
  31. Bao, N. (2021). More of the bulk from extremal area variations. Classical and Quantum Gravity, 38(4), Article 47001 https://dx.doi.org/10.1088/1361-6382/abcfd0
  32. Solovyov, V. & Saira, O. (2021). YBCO-on-Kapton: Material for High-Density Quan-tum Computer Interconnects with Ultra-Low Thermal Conductance. IEEE Transactions on Applied Superconductivity https://www.osti.gov/biblio/1764583
  33. Samulyak, R. (2021). Lagrangian particle model for 3D simulation of pellets and SPI fragments in tokamaks. Nuclear Fusion https://www.osti.gov/biblio/1762766
  34. Miryala, S. (2021). Waveform Processing Using Neural Network Algorithms on the Front-end Electronics. Jinst, 17 https://dx.doi.org/10.1088/1748-0221/17/01/C01039
  35. Bosviel, N. & Samulyak, R. (2021). Near-field models and simulations of pellet ablation in tokamaks. Physics of Plasmas https://www.osti.gov/biblio/1755145
  36. Kumar, P. & Samulyak, R. (2021). Evolution of the self-injection process in long wavelength infrared laser driven LWFA. Physics of Plasmas https://www.osti.gov/biblio/1755147

2020

  1. Acharya, A. & Pouchard, L. (2020). Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19. Journal of Chemical Information and Modeling, 60, 5832-5852 https://www.osti.gov/biblio/1755144
  2. Acharya, A. & Park, G. (2020). Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19. Journal of Chemical information and modeling https://dx.doi.org/10.1021/acs.jcim.0c1010
  3. Williams-Young, D. & Van Dam, H. (2020). On the Efficient Evaluation of the Exchange Correlation Potential on Graphics Processing Unit Clusters. Frontiers in Chemistry, 8 https://dx.doi.org/10.3389/fchem.2020.581058
  4. Yin, C. & Luo, X. (2020). Probabilistic evolution of stochastic dynamical systems: A meso-scale perspective. Science Direct, 89 https://www.osti.gov/biblio/1831448
  5. Kelly, C. (2020). Chimbuko: A Workflow-Level Scalable Performance Trace Analysis Tool. https://dx.doi.org/10.1145/3426462
  6. Huang, X. & Xu, W. (2020). Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images. IEEE Transactions on Visualization and Computer Graphics, 27(2), 1312-1321 https://www.osti.gov/biblio/1782541
  7. Agarwal, K. & Bao, N. (2020). Toy model for decoherence in the black hole information problem. Physical Review D, 102, Article 086017 https://dx.doi.org/10.1103/PhysRevD.102.086017
  8. Wu, L. & Robinson, I. (2020). Complex Imaging of Phase Domains by Deep Neural Network. International Union of Crystallography (IUCr) https://www.osti.gov/biblio/1677665
  9. Madireddy, S. & Park, J. (2020). In Situ Compression Artifact Removal in Scientific Data Using Deep Transfer Learning and Experience Replay. Machine Learning: Science and Technology https://www.osti.gov/biblio/1693391
  10. Castillo, P. & Yu, K. (2020). Numerical simulation of terahertz radiation by laser-driven plasma dipole oscillation. Spie , 11539 https://www.osti.gov/biblio/1813345
  11. Bao, N. (2020). Warping wormholes with dust: a metric construction of the Python's Lunch. Journal of High Energy Physics, Article 102 (2020) https://dx.doi.org/10.1007/JHEP09(2020)102
  12. Langston, M. & Lin, M. (2020). Approximate Inverse Chain Preconditioner: Iteration Count Case Study for Spectral Support Solvers. https://www.osti.gov/biblio/1764601
  13. Langston, M. & Lin, M. (2020). Approximate Inverse Chain Preconditioner: Iteration Count Case Study for Spectral Support Solvers. https://www.osti.gov/biblio/1673301
  14. Apra, E. & Van Dam, H. (2020). NWChem: Past, present, and future. The Journal of Chemical Physics, 152(18) https://dx.doi.org/10.1063/5.0004997
  15. Luckow, A. & Jha, S. (2020). Methods and Experiences for Developing Abstractions for Data-intensive, Scientific Applications. 2020 Ieee 34Th International Parallel and Distributed Processing Symposium Workshops (Ipdpsw 2020) https://www.osti.gov/biblio/1777424
  16. Ren, Y. (2020). Performance Analysis of Deep Learning Workloads on Leading-edge Systems. https://dx.doi.org/10.1109/PMBS49563.2019.00017
  17. Peterka, T. & Pouchard, L. (2020). Priority Research Directions for In Situ Data Management: Enabling Scientific Discovery from Diverse Data Sources. International Journal Of High Performance Computing Applications https://dx.doi.org/10.1177/1094342020913628
  18. Park, J. (2020). Machine Learning Prediction of Incidence of Alzheimer's Disease Using Large-Scale Administrative Health Data. npj Digital Medicine, Article 46 (2020) https://dx.doi.org/10.1038/s41746-020-0256-0
  19. Saira, O. (2020). Nonequilibrium thermodynamics of erasure with superconducting flux logic. Physical Review Research https://www.osti.gov/biblio/1593261
  20. Okwan, P. & Yoo, S. (2020). Statistical Analysis of Nutrient Loads from the Mississippi-Atchafalaya River Basin (MARB) to the Gulf of Mexico. Environments, 7(1) https://dx.doi.org/10.3390/environments7010008
  21. Saira, O. (2020). Modification of electron-phonon coupling by micromachining and suspension. Journal of Applied Physics, 127, Article 024307 https://dx.doi.org/10.1063/1.5132948

2019

  1. Ren, Y. (2020). Performance Analysis of Deep Learning Workloads on Leading-edge Systems. https://dx.doi.org/10.1109/PMBS49563.2019.00017
  2. Kaufman, A. & McGuigan, M. (2019). Quantum computation for early universe cosmology. 2019 New York Scientific Data Summit (Nysds) ​, 10(1109), 1-6 https://dx.doi.org/10.1109/NYSDS.2019.8909801
  3. Peltroche, J. & McGuigan, M. (2019). Quantum Computation and Visualization of Carbon Single and Double Nano-Rings. 2019 New York Scientific Data Summit (Nysds) ​, 1-6 https://dx.doi.org/10.1109/NYSDS.2019.8909754
  4. Miceli, R. & McGuigan, M. (2019). Effective matrix model for nuclear physics on a quantum computer. 2019 New York Scientific Data Summit (Nysds) ​, 1-4 https://dx.doi.org/10.1109/NYSDS.2019.8909693
  5. Miceli, R. & McGuigan, M. (2019). Effective matrix model for nuclear physics on a quantum computer. 2019 New York Scientific Data Summit (Nysds) ​, 1-4 https://dx.doi.org/10.1109/NYSDS.2019.8909693
  6. Miceli, R. & McGuigan, M. (2019). Thermo Field Dynamics on a Quantum Computer. 2019 New York Scientific Data Summit (Nysds) ​, 1-4 https://dx.doi.org/10.1109/NYSDS.2019.8909787
  7. Miceli, R. & McGuigan, M. (2019). Quantum computation of nanosheets in a background magnetic field for external control of nanosystems. 2019 New York Scientific Data Summit (Nysds) ​, 1-4 https://dx.doi.org/10.1109/NYSDS.2019.8909731
  8. Li, X. & Lin, Y. (2019). Picking Particles in Cryo-EM Micrographs without Knowing the Particle Size. IEEE Xplore https://dx.doi.org/10.1109/NYSDS.2019.8909792
  9. Park, J. (2019). CMed: Crowd Analytics for Medical Imaging Data. IEEE Transactions on Visualization and Computer Graphics https://www.osti.gov/biblio/1677652
  10. Kokkoniemi, R. & Saira, O. (2019). Nanobolometer with ultralow noise equivalent power. Communications Physics, 2(124), 1-8 https://dx.doi.org/10.1038/s42005-019-0225-6
  11. Kokkoniemi, R. & Saira, O. (2019). Nanobolometer with ultralow noise equivalent power. Communication Physics, 2, 1-8 https://dx.doi.org/10.1038/s42005-019-0225-6
  12. Bao, N. (2019). Multipartite reflected entropy. Journal of High Energy Physics, (10), Article 102 https://dx.doi.org/10.1007/JHEP10(2019)102
  13. Shahneous Bari, M. & Malik, A. (2019). Performance and energy impact of OpenMP runtime configurations on power constrained systems. Sustainable Computing: Informatics and Systems, 23, 1-12 https://www.osti.gov/biblio/1571400
  14. Bao, N. (2019). Eigenstate thermalization hypothesis and approximate quantum error correction. Journal of High Energy Physics https://www.osti.gov/biblio/1566871
  15. Wang, L. & Saira, O. (2019). Crossover between electron-phonon and boundary resistance limited thermal relaxation in copper films. Physical Review Applied, 12, 1-5 https://dx.doi.org/10.1103/PhysRevApplied.12.024051
  16. Bao, N. (2019). Towards bulk metric reconstruction from extremal area variations. Classical and Quantum Gravity, 36(18) https://www.osti.gov/biblio/1566869
  17. Bao, N. (2019). The holographic dual of Rényi relative entropy. Journal of High Energy Physics https://www.osti.gov/biblio/1566870
  18. Bao, N. (2019). Towards a bit threads derivation of holographic entanglement of purification. Journal of High Energy Physics https://www.osti.gov/biblio/1566868
  19. Kumar, P. & Samulyak, R. (2019). Simulation study of CO2 laser-plasma interactions and self-modulated wakefield acceleration. Physics of Plasmas , 26(7) https://www.osti.gov/biblio/1543396
  20. Xie, C. & Van Dam, H. (2019). Exploratory Visual Analysis of Anomalous Runtime Behavior in Streaming High Performance Computing Applications. International Conference on Computational Science https://www.osti.gov/biblio/1560000
  21. Xie, C. & Xu, W. (2019). Performance Visualization for TAU Instrumented Scientific Workflows. Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications , 3 https://dx.doi.org/10.5220/0006646803330340
  22. Wang, Y. & Yoo, S. (2019). Diagnosis and Prognosis of Alzheimer’s Disease Using Brain 2 Morphometry and White Matter Connectomes. NeuroImage: Clinical https://www.osti.gov/biblio/1514489
  23. Thio, Y. & Samulyak, R. (2019). Plasma Jet Driven Magneto-Inertial Fusion (PJMIF). Fusion Science and Technology. https://www.osti.gov/biblio/1506631
  24. Wysocki, D. & Fang, Y. (2019). Accelerating parameter inference with graphics processing units. Physical Review D, 99(8) https://www.osti.gov/biblio/1524547
  25. Pouchard, L. (2019). Computational reproducibility of scientific workflows at extreme scales. International Journal of High Performance Computing Applications https://www.osti.gov/biblio/1542776
  26. Calajó, G. & Fang, Y. (2019). Exciting a Bound State in the Continuum through Multiphoton Scattering Plus Delayed Quantum Feedback. Physical Review Letters, 122(7) https://www.osti.gov/biblio/1524549
  27. DeGennaro, A. (2019). Model Structural Inference using Local Dynamic Operators. International Journal for Uncertainty Quantification, 9(1), 59-83 https://www.osti.gov/biblio/1525383
  28. Cheng, S. & Xu, W. (2019). ColorMap(ND): A Data-Driven approach and tool for Mapping Multivariate data to color. IEEE Transactions on Visualization and Computer Graphics https://www.osti.gov/biblio/1491695
  29. Shih, W. & Samulyak, R. (2019). Simulation Study of the Influence of Experimental Variations on the Structure and Quality of Plasma Liners. Physics of Plasmas https://www.osti.gov/biblio/1492767
  30. Xie, C. & Xu, W. (2019). A Visual Analytics Framework for the Detection of Anomalous Call Stack Trees in High Performance Computing Applications. IEEE Transactions on Visualization and Computer Graphics, 25, Article 1 https://www.osti.gov/biblio/1489354
  31. Xie, C. & Xu, W. (2019). Visual Analytics of Heterogeneous Data Using Hypergraph Learning. Acm Transactions On Intelligent Systems and Technology, 10(1), Article 4 https://dx.doi.org/10.1145/3200765

2018

  1. Xie, C. & Xu, W. (2019). Performance Visualization for TAU Instrumented Scientific Workflows. Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications , 3 https://dx.doi.org/10.5220/0006646803330340
  2. Elghazoly, S. & McGuigan, M. (2018). Visualization and quantum computation of moire superconductivity in bilayer graphene, carbon nanocones and nanostrips. IEEE Conference Publication https://www.osti.gov/biblio/1489347
  3. Kocher, C. & McGuigan, M. (2018). Simulating 0+1 Dimensional Quantum Gravity on Quantum Computers: Mini-Superspace Quantum Cosmology and the World Line Approach in Quantum Field Theory. IEEE xplore https://www.osti.gov/biblio/1619263
  4. Kane, C. & McGuigan, M. (2018). Visualizing effective potentials and using the IBM-Q to study quantum field theory models in 0+1 dimensions. IEEE Xplore https://www.osti.gov/biblio/1619259
  5. Ortega, B. & McGuigan, M. (2018). Visualization and Simulation of Carbon Structures with Higher Genus. IEEE Xplore https://www.osti.gov/biblio/1619264
  6. Miceli, R. & McGuigan, M. (2018). Quantum Computation and Visualization of Hamiltonians Using Discrete Quantum Mechanics and IBM QISKit. IEEE Xplore https://www.osti.gov/biblio/1619265
  7. Ma, J. & Samulyak, R. (2018). Simulation studies of modulator for coherent electron cooling. Physical Review Accelerators and Beams, 21(11), Article 111001 https://dx.doi.org/10.1103/PhysRevAccelBeams.21.111001
  8. Penchoff, D. & Harrison, R. (2018). Structural Analysis of the Complexation of Uranyl, Neptunyl, Plutonyl, and Americyl with Cyclic Imide Dioximes. ACS Omega, 3(10), 1398-13993 https://dx.doi.org/10.1021/acsomega.8b02068
  9. Penchoff, D. & Harrison, R. (2018). Structural Characteristics, Population Analysis, and Binding Energies of [An(NO3)](2+) (with An = Ac to Lr). ACS Omega, 3(10), 14127-14143 https://dx.doi.org/10.1021/acsomega.8b01800
  10. Kasson, P. & Jha, S. (2018). Adaptive ensemble simulations of biomolecules. Current Opinion in Structural Biology https://dx.doi.org/10.1016/j.sbi.2018.09.005
  11. Song, S. & Lin, Y. (2018). Domain Adaptation for Convolutional Neural Networks Based Remote Sensing Scene Classification. IEEE Geoscience and Remote Sensing Letters https://www.osti.gov/biblio/1492766
  12. Elghazoly, S. & McGuigan, M. (2018). Visualization and Quantum Computation of Moire Superconductivity in Bilayer Graphene, Carbon Nanocones and Nanostrips. https://www.osti.gov/biblio/1619261
  13. Samaroo, A. & McGuigan, M. (2018). Using IBM-Q to Study and Visualize the Ground State Properties of the Su-Schrieffer-Heeger Model. https://www.osti.gov/biblio/1619262
  14. Fang, Y. (2018). FDTD: solving 1+1D delay PDE in parallel. Cpc https://www.osti.gov/biblio/1475143
  15. Bhattacharyya, S. & Katramatos, D. (2018). Why wait? Let's start computing while data is still on the wire. Future Generation Computer Systems, 89, 563-574 https://www.osti.gov/biblio/1619256
  16. Merzky, A. & Jha, S. (2018). Synapse: Synthetic Application Profiler and Emulator. Journal of Computational Science, 27 https://www.osti.gov/biblio/1488839
  17. Liu, Q. & Katramatos, D. (2018). Virtual Environment for Testing Software-Defined Networking Solutions for Scientific Workflows. AI-Science'18 Proceedings of the 1st International Workshop on Autonomous Infrastructure for Science , Article 3 https://www.osti.gov/biblio/1619257
  18. Samulyak, R. (2018). Lagrangian Particle Method for Compressible Fluid Dynamics. Journal of Computational Physics, 362, 1-19 https://dx.doi.org/10.1016/j.jcp.2018.02.004
  19. Juhas, P. (2018). PDFgetN3: Atomic Pair Distribution Functions From Neutron Powder Diffraction Data Using ad hoc Corrections. Journal of Applied Crystallography https://www.osti.gov/biblio/1439450
  20. Pouchard, L. (2018). Use Cases of Computational Reproducibility for Scientific Workflows at Exascale.
  21. Feng, H. (2018). Seasonal Differences in Trace Element Concentrations and Distribution in Spartina Alterniflora Root Tissue. Chemosphere, 204, 359-370 https://dx.doi.org/10.1016/j.chemosphere.2018.04.058
  22. Yu, K. (2018). Simulation of plasma loading of high-pressure RF cavities. Jinst, 13(01) https://www.osti.gov/biblio/1424991
  23. Rao, N. & Katramatos, D. (2018). Software-Defined Network Solutions for Science Scenarios: Performance Testing Framework and Measurements. ICDCN '18 Proceedings of the 19th International Conference on Distributed Computing and Networking , Article 53 https://www.osti.gov/biblio/1619258

2017

  1. Shahneous Bari, M. & Malik, A. (2019). Performance and energy impact of OpenMP runtime configurations on power constrained systems. Sustainable Computing: Informatics and Systems, 23, 1-12 https://www.osti.gov/biblio/1571400
  2. Bhattacharyya, S. & Katramatos, D. (2018). Why wait? Let's start computing while data is still on the wire. Future Generation Computer Systems, 89, 563-574 https://www.osti.gov/biblio/1619256
  3. Pouchard, L. (2018). Use Cases of Computational Reproducibility for Scientific Workflows at Exascale.
  4. Fratino, L. & Semon, P. (2017). Effects of interaction strength, doping, and frustration on the antiferromagnetic phase of the two-dimensional Hubbard Model. Physical Review B, Article 241109 https://www.osti.gov/biblio/1425185
  5. Hsu, S. & Samulyak, R. (2017). Experiment to Form and Characterize a Section of a Spherically Imploding Plasma Liner. IEEE Transactions on Plasma Science , PP(99) https://www.osti.gov/biblio/1426785
  6. Mishra, A. (2017). Benchmarking and Evaluating Unified Memory for OpenMP GPU Offloading. https://www.osti.gov/biblio/1412779
  7. Stephan, E. & Pouchard, L. (2017). A Scientific Data Provenance Harvester for Distributed Applications. Proceedings of the IEEE https://dx.doi.org/10.1109/NYSDS.2017.8085041
  8. Zhong, W. & Xu, W. (2017). Evolutionary Visual Analysis of Deep Neural Networks.
  9. Pouchard, L. (2017). Capturing provenance as a diagnostic tool for workflow performance evaluation and optimization. Proceedings of the IEEE https://dx.doi.org/10.1109/nysds.2017.8085043
  10. Fratino, L. & Semon, P. (2017). Signatures of the Mott transition in the antiferromagnetic state of the two-dimensional Hubbard model. https://www.osti.gov/biblio/1392250
  11. Lin, Y. (2017). Visual-Attention-Based Background Modeling for Detecting Infrequently Moving Objects. IEEE Transactions on Circuits and Systems for Video Technology, 27(6), 1208-1221 https://www.osti.gov/biblio/1491679
  12. Semon, P. & Semon, P. (2017). Validity of the local approximation in iron pnictides and chalcogenides. https://www.osti.gov/biblio/1392251
  13. Lin, Y. (2017). Cross-Domain Recognition by Identifying Joint Subspaces of Source Domain and Target Domain. IEEE Transactions on Cybernetics, 47(4), 1090-1101 https://www.osti.gov/biblio/1491680
  14. Yu, K. (2017). Simulation of beam-induced plasma in gas-filled RF cavities. https://www.osti.gov/biblio/1362161

2016

  1. Hao, H. (2016). Diverse Power Iteration Embeddings: Theory and Practice. https://www.osti.gov/biblio/1345738
  2. Wang, X. (2016). AP-Cloud: Adaptive Particle-in-Cloud Method for Optimal Solutions to Vlasov-Poisson Equation. https://www.osti.gov/biblio/1324260
  3. Li, W. (2016). Finite Element Model for Brittle Fracture and Fragmentation. https://www.osti.gov/biblio/1324261
  4. Samulyak, R. (2016). Second Order Upwind Lagrangian Particle Method for Euler Equations. https://www.osti.gov/biblio/1324262

2015

  1. Peng, Z. (2015). 3D Cloud Detection and Tracking System for Solar Forecast Using Multiple Sky Imagers. https://www.osti.gov/biblio/1193237
  2. Huang, H. (2015). Density-Aware Clustering Based on Aggregated Heat Kernel and Its Transformation. https://www.osti.gov/biblio/1210172
  3. Yanai, T. (2015). Multiresolution quantum chemistry in multiwavelet bases: Excited states from time-dependent Hartree-Fock and density functional theory via linear response. The Royal Society of Chemistry https://dx.doi.org/10.1039/C4CP05821F

2014

  1. Sharma, S. (2014). Joint Optimization of Session Grouping and Relay Node Selection for Network-Coded Cooperative Communications. https://www.osti.gov/biblio/1165721
  2. Huang, H. (2014). Physics-Based Anomaly Detection Defined on Manifold Space. https://www.osti.gov/biblio/1172089
  3. Appelquist, T. & Lin, M. (2014). Two-Color Theory with Novel Infrared Behavior. https://www.osti.gov/biblio/1132491

2013

  1. Yan, L. (2013). Combined Theoretical and Experimental Study of Band-Edge Control of Si thorough Surface Functionalization. https://www.osti.gov/biblio/1093774
  2. Michels-Clark, T. (2013). Analyzing diffuse scattering with supercomputers. https://www.osti.gov/biblio/1240717
  3. Glimm, J. (2013). New directions for Rayleigh-Taylor mixing. https://www.osti.gov/biblio/1124577
  4. Ren, Y. & Yu, D. (2013). Design and testbed evaluation of RDMA-based middleware for high-performance data transfer applications. https://www.osti.gov/biblio/1108590
  5. Gu, Y. (2013). Distributed Throughput Optimization for Large-Scale Scientific Workflows Under Fault-Tolerance Constraint. J. Grid Computing https://dx.doi.org/10.1007/s10723-013-9266-3
  6. Hyoungkeun, K. & Samulyak, R. (2013). On the structure of plasma liners for plasma jet induced magnetoinertial fusion. https://www.osti.gov/biblio/1093793

2012

  1. Hoisie, A. (2012). Report on the ASCR Workshop on Modeling and Simulation of Exascale Systems and Applications. https://www.osti.gov/biblio/1818915
  2. Feng, R. & Davenport, J. (2012). Viscous flow simulation in a stenosis model using discrete particle dynamics: a comparison between DPD and CFD. https://www.osti.gov/biblio/1052614

2011

  1. Sharma, S. (2011). Optimal Grouping and Matching for Network-Coded Cooperative Communications. IEEE Conference Publication, 722-728 https://www.osti.gov/biblio/1052624

2010

  1. Kaman, T. & Glimm, J. (2010). Initial Conditions for Turbulent Mixing Simulations. https://www.osti.gov/biblio/1019448
  2. Samulyak, R. (2010). Spherically symmetric simulation of plasma liner driven magnetoinertial fusion.. https://www.osti.gov/biblio/1095170
  3. Kilchyk, V. (2010). Baroclinic vortex sheet production by shocks and expansion waves. https://www.osti.gov/biblio/1000448
  4. Lim, H. & Glimm, J. (2010). Nonideal Rayleigh-Taylor Mixing. https://www.osti.gov/biblio/991721
  5. Liu, X. & Loffredo, L. (2010). Application-specific resource provisioning for wide-area disbributed computing. https://www.osti.gov/biblio/1088185
  6. Li, X. & Glimm, J. (2010). Study of Crystal Growth and Solute Precipitation through Front Tracking Method. https://www.osti.gov/biblio/1040423
  7. Luchko, T. & Simmerling, C. (2010). Three-Dimensional Molecular Theory of Solvation Coupled with Molecular Dynamics in Amber. https://www.osti.gov/biblio/1020830
  8. Lim, H. & Loffredo, L. (2010). Nearly discontinuous chaotic mixing. https://www.osti.gov/biblio/1088186

2009

  1. Jin, H. & Glimm, J. (2009). Weakly Compressible Two-Pressure Two-Phase Flow. https://www.osti.gov/biblio/1040354
  2. Glimm, J. (2009). Mathematical Perspectives. https://www.osti.gov/biblio/1020850
  3. Moges, M. & Yu, D. (2009). Grid scheduling divisible loads from two sources. https://www.osti.gov/biblio/1040332
  4. Kocharian, A. & Davenport, J. (2009). Spin-charge separation and electron pairing instabilities in Hubbard nanoclusters. https://www.osti.gov/biblio/1040137

About BrookhavenLabCS

CSI research and capabilities continue to expand the state of the art in high-performance computing, applied mathematics, data science, and Quantum Information Science to propel advances at the frontiers of science here at Brookhaven National Laboratory, throughout the nation, and across the world.