Massimiliano Lupo Pasini

40 papers A* 1B 3Journal 31Unranked 5

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data. CoRR Massimiliano Lupo Pasini, Jong Youl Choi, Pei Zhang, Kshitij Mehta, Rylie Weaver, Ashwin M. Aji, Karl W. Schulz, Jorda Polo, Prasanna Balaprakash
2025	J	`jnl`	Scalable training of trustworthy and energy-efficient predictive graph foundation models for atomistic materials modeling: a case study with HydraGNN. J. Supercomput. Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta, Pei Zhang, David M. Rogers, Jonghyun Bae, Khaled Z. Ibrahim, Ashwin M. Aji, Karl W. Schulz, Jordà Polo, Prasanna Balaprakash
2025	A*	`conf`	Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling. DAC Chaojian Li, Zhifan Ye, Massimiliano Lupo Pasini, Jong Youl Choi, Cheng Wan, Prasanna Balaprakash
2025	J	`jnl`	Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling. CoRR Chaojian Li, Zhifan Ye, Massimiliano Lupo Pasini, Jong Youl Choi, Cheng Wan, Yingyan Celine Lin, Prasanna Balaprakash
2025	J	`jnl`	Two-Level Sketching Alternating Anderson acceleration for Complex Physics Applications. CoRR Nicolás A. Barnafi, Massimiliano Lupo Pasini
2025	J	`jnl`	When Does Global Attention Help? A Unified Empirical Study on Atomistic Graph Learning. CoRR Arindam Chowdhury, Massimiliano Lupo Pasini
2024	J	`jnl`	A Perspective on Scalable AI on High-Performance Computing and Leadership Class Supercomputing Facilities [Industrial and Governmental Activities]. IEEE Comput. Intell. Mag. Massimiliano Lupo Pasini
2024	J	`jnl`	AI for Materials Design and Discovery Using Atomistic Scale Information [Industrial and Governmental Activities]. IEEE Comput. Intell. Mag. Massimiliano Lupo Pasini
2024	J	`jnl`	Anderson acceleration with approximate calculations: Applications to scientific computing. Numer. Linear Algebra Appl. Massimiliano Lupo Pasini, M. Paul Laiu
2024	—	`conf`	MDLoader: A Hybrid Model-Driven Data Loader for Distributed Graph Neural Network Training. SC Workshops Jonghyun Bae, Jong Youl Choi, Massimiliano Lupo Pasini, Kshitij Mehta, Pei Zhang, Khaled Z. Ibrahim
2024	—	`conf`	MDLoader: A Hybrid Model-driven Data Loader for Distributed Deep Neural Networks Training. IPDPS (Workshops) Jonghyun Bae, Jong Youl Choi, Massimiliano Lupo Pasini, Kshitij Mehta, Khaled Z. Ibrahim
2024	J	`jnl`	Scalable Training of Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN. CoRR Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta, Pei Zhang, David M. Rogers, Jonghyun Bae, Khaled Z. Ibrahim, Ashwin M. Aji, Karl W. Schulz, Jorda Polo, Prasanna Balaprakash
2024	—	`conf`	Scaling Ensembles of Data-Intensive Quantum Chemical Calculations for Millions of Molecules. IPDPS (Workshops) Kshitij Mehta, Massimiliano Lupo Pasini, Stephan Irle, Pilsun Yoo, Frédéric Suter, Dmitry Ganyushin, Scott Klasky
2024	J	`jnl`	Transferring predictions of formation energy across lattices of increasing size. Mach. Learn. Sci. Technol. Massimiliano Lupo Pasini, Mariia Karabin, Markus Eisenbach
2023	J	`jnl`	A deep learning approach for adaptive zoning. CoRR Massimiliano Lupo Pasini, Luka Malenica, Kwitae Chong, Stuart R. Slattery
2023	—	`conf`	DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets. SC Workshops Jong Youl Choi, Massimiliano Lupo Pasini, Pei Zhang, Kshitij Mehta, Frank Liu, Jonghyun Bae, Khaled Ibrahim
2023	J	`jnl`	DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. CoRR Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete Luferenko, Divya Kumar, Jonathan A. Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael W. Irvin, J. Gregory Pauloski, Logan T. Ward, Valérie Hayot-Sasson, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian T. Foster, James J. Davis, Michael E. Papka, Thomas S. Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi A. Hanson, Thomas E. Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton D. Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin M. Aji, Angela Dalton, Michael J. Schulte, Karl W. Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, Rick Stevens
2023	J	`jnl`	Hierarchical Model Reduction Driven by Machine Learning for Parametric Advection-Diffusion-Reaction Problems in the Presence of Noisy Data. J. Sci. Comput. Massimiliano Lupo Pasini, Simona Perotto
2023	J	`jnl`	Stable parallel training of Wasserstein conditional generative adversarial neural networks. J. Supercomput. Massimiliano Lupo Pasini, Junqi Yin
2022	J	`jnl`	A deep learning approach for detection and localization of leaf anomalies. CoRR Davide Calabrò, Massimiliano Lupo Pasini, Nicola Ferro, Simona Perotto
2022	J	`jnl`	A deep learning approach to solve forward differential problems on graphs. CoRR Yuanyuan Zhao, Massimiliano Lupo Pasini
2022	J	`jnl`	Anderson acceleration with approximate calculations: applications to scientific computing. CoRR Massimiliano Lupo Pasini, M. Paul Laiu
2022	B	`conf`	Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models. SMC Andrew E. Blanchard, Pei Zhang, Debsindhu Bhowmik, Kshitij Mehta, John Gounley, Samuel Temple Reeve, Stephan Irle, Massimiliano Lupo Pasini
2022	J	`jnl`	Hierarchical model reduction driven by machine learning for parametric advection-diffusion-reaction problems in the presence of noisy data. CoRR Massimiliano Lupo Pasini, Simona Perotto
2022	B	`conf`	Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials. SMC Markus Eisenbach, Mariia Karabin, Massimiliano Lupo Pasini, Junqi Yin
2022	J	`jnl`	Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems Mach. Learn. Sci. Technol. Massimiliano Lupo Pasini, Pei Zhang, Samuel Temple Reeve, Jong Youl Choi
2022	J	`jnl`	Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems. CoRR Massimiliano Lupo Pasini, Pei Zhang, Samuel Temple Reeve, Jong Youl Choi
2022	J	`jnl`	Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules. CoRR Jong Youl Choi, Pei Zhang, Kshitij Mehta, Andrew E. Blanchard, Massimiliano Lupo Pasini
2022	J	`jnl`	Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules. J. Cheminformatics Jong Youl Choi, Pei Zhang, Kshitij Mehta, Andrew E. Blanchard, Massimiliano Lupo Pasini
2022	J	`jnl`	Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks. CoRR Massimiliano Lupo Pasini, Junqi Yin
2021	J	`jnl`	A scalable algorithm for the optimization of neural network architectures. Parallel Comput. Massimiliano Lupo Pasini, Junqi Yin, Ying Wai Li, Markus Eisenbach
2021	B	`conf`	Fast and Accurate Predictions of Total Energy for Solid Solution Alloys with Graph Convolutional Neural Networks. SMC Massimiliano Lupo Pasini, Marko Burcul, Samuel Temple Reeve, Markus Eisenbach, Simona Perotto
2021	J	`jnl`	Scalable Balanced Training of Conditional Generative Adversarial Neural Networks on Image Data. CoRR Massimiliano Lupo Pasini, Vittorio Gabbi, Junqi Yin, Simona Perotto, Nouamane Laanait
2021	J	`jnl`	Scalable balanced training of conditional generative adversarial neural networks on image data. J. Supercomput. Massimiliano Lupo Pasini, Vittorio Gabbi, Junqi Yin, Simona Perotto, Nouamane Laanait
2021	J	`jnl`	Stable Anderson Acceleration for Deep Learning. CoRR Massimiliano Lupo Pasini, Junqi Yin, Viktor Reshniak, Miroslav Stoyanov
2021	—	`conf`	Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks : *Full/Regular Research Paper submission for the symposium CSCI-ISAI: Artificial Intelligence. CSCI Massimiliano Lupo Pasini, Junqi Yin
2020	J	`jnl`	A parallel strategy for density functional theory computations on accelerated nodes. Parallel Comput. Massimiliano Lupo Pasini, Bruno Turcksin, Wenjun Ge, Jean-Luc Fattebert
2019	J	`jnl`	A greedy constructive algorithm for the optimization of neural network architectures. CoRR Massimiliano Lupo Pasini, Junqi Yin, Ying Wai Li, Markus Eisenbach
2019	J	`jnl`	Convergence analysis of Anderson-type acceleration of Richardson's iteration. Numer. Linear Algebra Appl. Massimiliano Lupo Pasini
2017	J	`jnl`	Analysis of Monte Carlo accelerated iterative methods for sparse linear systems. Numer. Linear Algebra Appl. Michele Benzi, Thomas M. Evans, Steven P. Hamilton, Massimiliano Lupo Pasini, Stuart R. Slattery