| Analysis of Monte Carlo accelerated iterative methods for sparse linear systems M Benzi, TM Evans, SP Hamilton, M Lupo Pasini, SR Slattery Numerical Linear Algebra with Applications 24 (3), e2088, 2017 | 34 | 2017 |
| Convergence analysis of Anderson‐type acceleration of Richardson's iteration M Lupo Pasini Numerical Linear Algebra with Applications 26 (4), e2241, 2019 | 24 | 2019 |
| Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems M Lupo Pasini, P Zhang, ST Reeve, JY Choi Machine Learning: Science and Technology 3 (2), 025007, 2022 | 21 | 2022 |
| Fast and stable deep-learning predictions of material properties for solid solution alloys M Lupo Pasini, YW Li, J Yin, J Zhang, K Barros, M Eisenbach Journal of Physics: Condensed Matter 33 (8), 084005, 2020 | 19 | 2020 |
| DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies SL Song, B Kruft, M Zhang, C Li, S Chen, C Zhang, M Tanaka, X Wu, ... arXiv preprint arXiv:2310.04610, 2023 | 12 | 2023 |
| Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules JY Choi, P Zhang, K Mehta, A Blanchard, M Lupo Pasini Journal of Cheminformatics 14 (70), https://trebuchet.public.springernature., 2022 | 12 | 2022 |
| A scalable algorithm for the optimization of neural network architectures M Lupo Pasini, J Yin, YW Li, M Eisenbach Parallel Computing 104, 102788, 2021 | 11 | 2021 |
| HydraGNN M Lupo Pasini, ST Reeve, P Zhang, JY Choi https://doi.org/10.11578/dc.20211019.2 20211019, 2021 | 10* | 2021 |
| Hierarchical model reduction driven by a proper orthogonal decomposition for parametrized advection-diffusion-reaction problems M Lupo Pasini, S Perotto Electronic transactions on numerical analysis ETNA 55, 187-212, 2022 | 8 | 2022 |
| Graph neural networks predict energetic and mechanical properties for models of solid solution metal alloy phases M Lupo Pasini, GS Jung, S Irle Computational Materials Science 224, 112141, 2023 | 7 | 2023 |
| Anderson Acceleration for Distributed Training of Deep Learning Models M Lupo Pasini, J Yin, V Reshniak, M Stoyanov SoutheastCon 2022, 289-295, 2022 | 7 | 2022 |
| Two excited-state datasets for quantum chemical UV-vis spectra of organic molecules M Lupo Pasini, K Mehta, P Yoo, S Irle Nature Scientific Data 10 (546), 2023 | 6 | 2023 |
| Fast and accurate predictions of total energy for solid solution alloys with graph convolutional neural networks M Lupo Pasini, M Burc̆ul, ST Reeve, M Eisenbach, S Perotto Smoky Mountains Computational Sciences and Engineering Conference, 79-98, 2021 | 6 | 2021 |
| Stable Anderson Acceleration for Deep Learning M Lupo Pasini, J Yin, V Reshniak, M Stoyanov arXiv e-prints, arXiv: 2110.14813, 2021 | 5* | 2021 |
| Scalable balanced training of conditional generative adversarial neural networks on image data M Lupo Pasini, V Gabbi, J Yin, Perotto, Simona, N Laanait Journal of Supercomputing 77 (11), 13358-13384, 2021 | 4 | 2021 |
| A parallel strategy for density functional theory computations on accelerated nodes M Lupo Pasini, B Turcksin, W Ge, JL Fattebert Parallel Computing 100, 102703, 2020 | 4 | 2020 |
| Deep learning workflow for the inverse design of molecules with specific optoelectronic properties P Yoo, D Bhowmik, K Mehta, P Zhang, F Liu, M Lupo Pasini, S Irle Scientific Reports 13 (1), 20031, 2023 | 3 | 2023 |
| DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets JY Choi, M Lupo Pasini, P Zhang, K Mehta, F Liu, J Bae, K Ibrahim Proceedings of the SC'23 Workshops of The International Conference on High …, 2023 | 3 | 2023 |
| Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models AE Blanchard, P Zhang, D Bhowmik, K Mehta, J Gounley, ST Reeve, ... Accelerating Science and Engineering Discoveries Through Integrated Research …, 2023 | 3 | 2023 |
| Hierarchical model reduction driven by machine learning for parametric advection-diffusion-reaction problems in the presence of noisy data M Lupo Pasini, S Perotto Journal of Scientific Computing 94 (36), 1-22, 2023 | 3 | 2023 |
Kshitij MehtaComputer Scientist, Oak Ridge National LabE-mailová adresa ověřena na: ornl.gov