ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification across the Periodic Table

Advanced E(3)-equivariant and transformer architectures are supported in addition to 2D models, with all classes including built-in uncertainty quantification through deterministic and statistical measures. We benchmark our protocols for ML model training against representative data sets from organic, inorganic, coordination, and biological chemistry, achieving competitive and SOTA performance relative to literature baselines and favorable scaling to millions of molecules.

The entire workflow is exposed through a concise command-line interface, lowering the barrier to entry for nonexpert users. We anticipate ElemeNet will empower noncomputational researchers to leverage modern deep learning methods across the chemical and physical sciences.