Towards Foundation Models for Materials Science: The Open MatSci ML Toolkit. - Kin Long Kelvin Lee; Carmelo Gonzales; Matthew Spellings; Mikhail Galkin 001; Santiago Miret; Nalini Kumar (SC Workshops, 2023)

Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks. - Daniel T. Levy; Sékou-Oumar Kaba; Carmelo Gonzales; Santiago Miret; Siamak Ravanbakhsh (CoRR, 2023)

MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling. - Kin Long Kelvin Lee; Carmelo Gonzales; Marcel Nassar; Matthew Spellings; Mikhail Galkin 001; Santiago Miret (CoRR, 2023)

The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science. - Santiago Miret; Kin Long Kelvin Lee; Carmelo Gonzales; Marcel Nassar; Matthew Spellings (Trans. Mach. Learn. Res., 2023)

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials. - Santiago Miret; Kin Long Kelvin Lee; Carmelo Gonzales; Sajid Mannan; N. M. Anoop Krishnan (CoRR, 2025)