Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets. - Dominique Beaini; Shenyang Huang; Joao Alex Cunha; Zhiyi Li; Gabriela Moisescu-Pareja; Oleksandr Dymov; Samuel Maddrell-Mander; Callum McLean; Frederik Wenkel; Luis Müller; Jama Hussein Mohamud; Ali Parviz; et al. (CoRR, 2023)

Gotta be SAFE: A New Framework for Molecular Design. - Emmanuel Noutahi; Cristian Gabellini; Michael Craig; Jonathan S. C. Lim; Prudencio Tossou (CoRR, 2023)

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets. - Dominique Beaini; Shenyang Huang; Joao Alex Cunha; Zhiyi Li; Gabriela Moisescu-Pareja; Oleksandr Dymov; Samuel Maddrell-Mander; Callum McLean; Frederik Wenkel; Luis Müller; Jama Hussein Mohamud; Ali Parviz; et al. (ICLR, 2024)

OpenQDC: Open Quantum Data Commons. - Cristian Gabellini; Nikhil Shenoy; Stephan Thaler; Semih Cantürk; Daniel McNeela; Dominique Beaini; Michael M. Bronstein; Prudencio Tossou (CoRR, 2024)

Implicit Delta Learning of High Fidelity Neural Network Potentials. - Stephan Thaler; Cristian Gabellini; Nikhil Shenoy; Prudencio Tossou (CoRR, 2024)

Self-Refining Training for Amortized Density Functional Theory. - Majdi Hassan; Cristian Gabellini; Hatem Helal; Dominique Beaini; Kirill Neklyudov (CoRR, 2025)