ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table
Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species. This work introduces ElemeNet, a unified, general-purpose software package for molecular machine learning. The ElemeNet software package enables the training of advanced ML models for diverse properties and datasets with an enlarged range of elemental compositions. We define molecular representations compatible with elements 1-100, supporting diverse organometallic and biological systems in addition to organic chemistry already well-served by...
| Authors | Jacob W. Toney, Samir Darouich, Yiran Wang et al. |
|---|---|
| Primary category | physics.chem-ph |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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