Analysis of Monte Carlo accelerated iterative methods for sparse linear systems. - Michele Benzi; Thomas M. Evans 001; Steven P. Hamilton; Massimiliano Lupo Pasini; Stuart R. Slattery (Numer. Linear Algebra Appl., 2017)

A greedy constructive algorithm for the optimization of neural network architectures. - Massimiliano Lupo Pasini; Junqi Yin; Ying Wai Li; Markus Eisenbach 002 (CoRR, 2019)

Convergence analysis of Anderson-type acceleration of Richardson's iteration. - Massimiliano Lupo Pasini (Numer. Linear Algebra Appl., 2019)

A parallel strategy for density functional theory computations on accelerated nodes. - Massimiliano Lupo Pasini; Bruno Turcksin; Wenjun Ge; Jean-Luc Fattebert (Parallel Comput., 2020)

Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks : *Full/Regular Research Paper submission for the symposium CSCI-ISAI: Artificial Intelligence. - Massimiliano Lupo Pasini; Junqi Yin (CSCI, 2021)

Fast and Accurate Predictions of Total Energy for Solid Solution Alloys with Graph Convolutional Neural Networks. - Massimiliano Lupo Pasini; Marko Burcul; Samuel Temple Reeve; Markus Eisenbach 002; Simona Perotto (SMC, 2021)

Stable Anderson Acceleration for Deep Learning. - Massimiliano Lupo Pasini; Junqi Yin; Viktor Reshniak; Miroslav Stoyanov (CoRR, 2021)

A scalable algorithm for the optimization of neural network architectures. - Massimiliano Lupo Pasini; Junqi Yin; Ying Wai Li; Markus Eisenbach 002 (Parallel Comput., 2021)

Scalable balanced training of conditional generative adversarial neural networks on image data. - Massimiliano Lupo Pasini; Vittorio Gabbi; Junqi Yin; Simona Perotto; Nouamane Laanait (J. Supercomput., 2021)

Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials. - Markus Eisenbach 002; Mariia Karabin; Massimiliano Lupo Pasini; Junqi Yin (SMC, 2022)

Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models. - Andrew E. Blanchard; Pei Zhang; Debsindhu Bhowmik; Kshitij Mehta; John Gounley; Samuel Temple Reeve; Stephan Irle; Massimiliano Lupo Pasini (SMC, 2022)

Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems. - Massimiliano Lupo Pasini; Pei Zhang; Samuel Temple Reeve; Jong Youl Choi (CoRR, 2022)

A deep learning approach for detection and localization of leaf anomalies. - Davide Calabrò; Massimiliano Lupo Pasini; Nicola Ferro 002; Simona Perotto (CoRR, 2022)

A deep learning approach to solve forward differential problems on graphs. - Yuanyuan Zhao; Massimiliano Lupo Pasini (CoRR, 2022)

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules. - Jong Youl Choi; Pei Zhang; Kshitij Mehta; Andrew E. Blanchard; Massimiliano Lupo Pasini (J. Cheminformatics, 2022)

Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems *. - Massimiliano Lupo Pasini; Pei Zhang; Samuel Temple Reeve; Jong Youl Choi (Mach. Learn. Sci. Technol., 2022)

DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets. - Jong Youl Choi; Massimiliano Lupo Pasini; Pei Zhang; Kshitij Mehta; Frank Liu 001; Jonghyun Bae; Khaled Ibrahim (SC Workshops, 2023)

A deep learning approach for adaptive zoning. - Massimiliano Lupo Pasini; Luka Malenica; Kwitae Chong; Stuart R. Slattery (CoRR, 2023)

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. - Shuaiwen Leon Song; Bonnie Kruft; Minjia Zhang; Conglong Li; Shiyang Chen 004; Chengming Zhang 006; Masahiro Tanaka; Xiaoxia Wu; Jeff Rasley; Ammar Ahmad Awan; Connor Holmes; Martin Cai; et al. (CoRR, 2023)

Hierarchical Model Reduction Driven by Machine Learning for Parametric Advection-Diffusion-Reaction Problems in the Presence of Noisy Data. - Massimiliano Lupo Pasini; Simona Perotto (J. Sci. Comput., 2023)

Stable parallel training of Wasserstein conditional generative adversarial neural networks. - Massimiliano Lupo Pasini; Junqi Yin (J. Supercomput., 2023)

MDLoader: A Hybrid Model-driven Data Loader for Distributed Deep Neural Networks Training. - Jonghyun Bae; Jong Youl Choi; Massimiliano Lupo Pasini; Kshitij Mehta; Khaled Z. Ibrahim (IPDPS (Workshops), 2024)

Scaling Ensembles of Data-Intensive Quantum Chemical Calculations for Millions of Molecules. - Kshitij Mehta; Massimiliano Lupo Pasini; Stephan Irle; Pilsun Yoo; Frédéric Suter; Dmitry Ganyushin; Scott Klasky (IPDPS (Workshops), 2024)

MDLoader: A Hybrid Model-Driven Data Loader for Distributed Graph Neural Network Training. - Jonghyun Bae; Jong Youl Choi; Massimiliano Lupo Pasini; Kshitij Mehta; Pei Zhang; Khaled Z. Ibrahim (SC Workshops, 2024)

Scalable Training of Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN. - Massimiliano Lupo Pasini; Jong Youl Choi; Kshitij Mehta; Pei Zhang; David M. Rogers 001; Jonghyun Bae; Khaled Z. Ibrahim; Ashwin M. Aji; Karl W. Schulz; Jorda Polo; Prasanna Balaprakash (CoRR, 2024)

Transferring predictions of formation energy across lattices of increasing size. - Massimiliano Lupo Pasini; Mariia Karabin; Markus Eisenbach 002 (Mach. Learn. Sci. Technol., 2024)

AI for Materials Design and Discovery Using Atomistic Scale Information [Industrial and Governmental Activities]. - Massimiliano Lupo Pasini (IEEE Comput. Intell. Mag., 2024)

A Perspective on Scalable AI on High-Performance Computing and Leadership Class Supercomputing Facilities [Industrial and Governmental Activities]. - Massimiliano Lupo Pasini (IEEE Comput. Intell. Mag., 2024)

Anderson acceleration with approximate calculations: Applications to scientific computing. - Massimiliano Lupo Pasini; M. Paul Laiu (Numer. Linear Algebra Appl., 2024)

Scalable training of trustworthy and energy-efficient predictive graph foundation models for atomistic materials modeling: a case study with HydraGNN. - Massimiliano Lupo Pasini; Jong Youl Choi; Kshitij Mehta; Pei Zhang; David M. Rogers 001; Jonghyun Bae; Khaled Z. Ibrahim; Ashwin M. Aji; Karl W. Schulz; Jordà Polo; Prasanna Balaprakash (J. Supercomput., 2025)

Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling. - Chaojian Li; Zhifan Ye; Massimiliano Lupo Pasini; Jong Youl Choi; Cheng Wan 005; Yingyan Celine Lin; Prasanna Balaprakash (CoRR, 2025)

Two-Level Sketching Alternating Anderson acceleration for Complex Physics Applications. - Nicolás A. Barnafi; Massimiliano Lupo Pasini (CoRR, 2025)

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data. - Massimiliano Lupo Pasini; Jong Youl Choi; Pei Zhang; Kshitij Mehta; Rylie Weaver; Ashwin M. Aji; Karl W. Schulz; Jorda Polo; Prasanna Balaprakash (CoRR, 2025)