21 citations · 38 across the 17 of their papers we have counts for
19 papers
Automated Data Readiness for Scientific AI
Sean R. Wilkinson, Valentine G. Anantharaj, Jong Youl Choi +8
Leadership computing facilities steward large-scale scientific datasets that routinely require substantial transformation before serving as AI training data. However, no existing f…
Scaling Novel Graph Generation via Lightweight Structure-Guided Autoregressive Models
Alessio Barboni, Massimiliano Lupo Pasini, Bishal Lakha +1
Generating realistic and diverse graphs is a key problem in machine learning, with applications in molecular discovery, circuit design, cybersecurity, and beyond. However, current…
Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data
Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta +8
We present an exascale workflow for materials discovery using atomistic graph foundation models built on HydraGNN. We jointly train on 16 open first-principles datasets (544+ milli…
When Does Global Attention Help? A Unified Empirical Study on Atomistic Graph Learning
Arindam Chowdhury, Massimiliano Lupo Pasini
Graph neural networks (GNNs) are widely used as surrogates for costly experiments and first-principles simulations to study the behavior of compounds at atomistic scale, and their…
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 +6
Graph foundation models using graph neural networks promise sustainable, efficient atomistic modeling. To tackle challenges of processing multi-source, multi-fidelity data during p…
Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling
Chaojian Li, Zhifan Ye, Massimiliano Lupo Pasini +4
Atomistic materials modeling is a critical task with wide-ranging applications, from drug discovery to materials science, where accurate predictions of the target material property…