Portrait of Jamal Abou Haibeh

Jamal Abou Haibeh

Collaborating Alumni - McGill University
Supervisor
Yoshua Bengio
Research Topics
AI for Science
Condensed Matter Physics
Deep Learning
Graph Neural Networks
Optimization
Google Scholar
GitHub

Publications

OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes
F'elix Therrien
Jamal Abou Haibeh
Divya Sharma
Rhiannon Hendley
Alex Hern'andez-Garc'ia
Sun Sun
Alain Tchagang
Jiang Su
Samuel Huberman
Yoshua Bengio
Hongyu Guo
Homin Shin
Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future thanks to their higher… (see more) theoretical energy density and improved safety. However, their adoption is currently hindered by their lower effective ionic conductivity, a quantity that governs charge and discharge rates. Identifying highly ion-conductive materials using conventional theoretical calculations and experimental validation is both time-consuming and resource-intensive. While machine learning holds the promise to expedite this process, relevant ionic conductivity and structural data is scarce. Here, we present OBELiX, a domain-expert-curated database of
2025-02-20
ArXiv (preprint)
arxiv.org
arxiv.org
OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes
Félix Therrien
Jamal Abou Haibeh
Divya Sharma
Rhiannon Hendley
Alex Hernandez-Garcia
Sun Sun
Alain Tchagang
Jiang Su
Samuel Huberman
Yoshua Bengio
Hongyu Guo
Homin Shin
Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future thanks to their higher… (see more) theoretical energy density and improved safety. However, their adoption is currently hindered by their lower effective ionic conductivity, a quantity that governs charge and discharge rates. Identifying highly ion-conductive materials using conventional theoretical calculations and experimental validation is both time-consuming and resource-intensive. While machine learning holds the promise to expedite this process, relevant ionic conductivity and structural data is scarce. Here, we present OBELiX, a domain-expert-curated database of
2025-02-20
ArXiv (preprint)
doi.org
arxiv.org