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

2026-06-29

AI Summary

This work proposes a unified molecular machine learning framework that overcomes the limitations of existing models, which are often confined to specific codebases and struggle to generalize across the full periodic table or diverse molecular properties. The framework supports elements 1–100, encompassing organic, inorganic, coordination, and biomolecular systems, and enables predictions at atomic, bond, molecular, and functional group levels. It natively incorporates conditional modeling of charge and spin states and uniquely integrates E(3)-equivariant networks, Transformers, and 2D graph neural networks within a single architecture, while combining both aleatoric and epistemic uncertainty quantification. Evaluated across multiple chemical benchmarks, the model matches or exceeds state-of-the-art performance, scales to datasets containing millions of molecules, and significantly lowers the barrier to entry for researchers without computational expertise.

Abstract

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 the Chemprop ML toolkit. As well as more common atom-, bond-, and molecule-level predictions, we introduce moiety predictions. We also natively define optional conditioning on charge and spin states. Advanced E(3)-equivariant and transformer architectures are supported, as well as classical 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 datasets 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 non-expert users. We anticipate ElemeNet will empower non-computational researchers to leverage modern deep learning methods across the chemical and physical sciences.

Problem

• molecular machine learning
• diverse chemical species
• elemental composition
• software package
• non-expert accessibility

Innovation

• ElemeNet
• multiscale molecular representation
• uncertainty quantification
• E(3)-equivariant architecture
• moiety-level prediction