Beyond Energies and Forces: OMol-Descriptors-4M, an Open Post-DFT Pipeline and Evaluation Protocol for Machine Learning in Chemistry

We present $\text{generator}$, an end-to-end computational chemistry post-analysis framework that processes repositories of molecular DFT wavefunctions and yields six partial-charge schemes (Hirshfeld, CM5, ADCH, Becke, Mulliken, Loewdin), four bond-order schemes, full QTAIM topology, fuzzy descriptors, and ORCA-derived globals. We built this by wrapping per-tier analysis routines around Multiwfn and ORCA's $\text{orca2mkl}$ utility, packaging the result into a scalable workflow for HPC clusters. Users can select compute tiers to trade fidelity for cost, then compact the resulting calculations into portable sharded Lightning Memory-Mapped Databases (LMDBs) keyed by job identity for downstream analysis and machine learning. To demonstrate the pipeline, we processed the recently-released OMol25 electronic structure dataset: a 4M-structure public subset of the 110M+-calculation 2025 release that ships the complete DFT outputs, including density matrices, energy gradients, ORCA $\text{.out}$ files, and $\text{.gbw}$ wavefunctions. By post-processing the wavefunctions, we extract atom-, bond-, and molecule-level descriptors across 4M structures spanning 34 chemical verticals, supplementing the original dataset's energy and force labels with physically-interpretable quantities. We name this dataset OMol-Descriptors-4M and present it as the first multi-vertical descriptor dataset at this scale beyond energies and forces. Analogous to the source dataset, we establish an evaluation protocol with explicit evaluative claims and scope boundaries: composition-ordered train/val/test splits via blake2b-hashed molecular formula and five domain-specific held-out stress tests (metal-ligand bond pairs, lanthanide-ligand bond pairs, reactivity, large systems, and high-charge regimes). Using this dataset, we establish a baseline set of models for future development and stress test them using these evaluation sets.