| --- |
| license: cc-by-4.0 |
| pretty_name: "Crystal-RL: surrogate-vs-DFT effective-mass gap (negative-result dataset)" |
| tags: |
| - materials-science |
| - effective-mass |
| - dft |
| - quantum-espresso |
| - jarvis |
| - reinforcement-learning |
| - surrogate-model |
| - negative-results |
| - methodology |
| - tabular |
| task_categories: |
| - tabular-regression |
| - tabular-classification |
| language: |
| - en |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: bootstrap_signed_mstar |
| data_files: |
| - split: train |
| path: "data/bootstrap_signed_mstar.parquet" |
| default: true |
| - config_name: surrogate_dft_pairs |
| data_files: |
| - split: train |
| path: "data/surrogate_dft_pairs.parquet" |
| --- |
| |
| # Crystal-RL: surrogate-vs-DFT effective-mass gap |
|
|
| Two related tables from the [`crystal-rl`](https://github.com/bshepp/crystal-rl) |
| materials-discovery project: |
|
|
| 1. **`bootstrap_signed_mstar`** — 794 Quantum ESPRESSO DFT calculations on |
| perturbed semiconductor crystal structures, with **signed** effective |
| mass values (681 negative, 113 positive). Signed-m\* DFT data is rare: |
| JARVIS, Materials Project, and AFLOW all store magnitudes only, which |
| destroys the sign that distinguishes band-inverted from normal-curvature |
| bands. This subset is the primary reusable artifact for anyone training |
| signed-m\* surrogate models. |
|
|
| 2. **`surrogate_dft_pairs`** — 24 surrogate-prediction-vs-DFT-truth pairs |
| across three independent RL+surrogate validation rounds. This subset |
| exists to support a methodological claim: **across three independent |
| pipeline variants** (unsigned-m\* baseline, signed-m\* with bootstrap data |
| absent, signed-m\* with bootstrap data loaded), **zero of 24 surrogate- |
| selected candidates DFT-validated as low-positive-effective-mass |
| semiconductors**. 19 of 24 instead showed negative DFT band curvature |
| (band-inversion signature). The dataset is published so other groups can |
| study the gap directly. |
|
|
| ## Dataset summary |
|
|
| | Subset | Records | What's in it | |
| |---|---|---| |
| | `bootstrap_signed_mstar` | 794 | Per-structure DFT outputs + 156-dim structural fingerprint | |
| | `surrogate_dft_pairs` | 24 | Per-candidate surrogate prediction *and* DFT measurement | |
|
|
| Both subsets load with the standard 🤗 Datasets library: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Default config (the bootstrap table) |
| boot = load_dataset("bshepp/rl-surrogate-dft-gap", split="train") |
| |
| # Explicit subset |
| pairs = load_dataset("bshepp/rl-surrogate-dft-gap", |
| "surrogate_dft_pairs", split="train") |
| ``` |
|
|
| ## Subsets |
|
|
| ### `bootstrap_signed_mstar` (794 rows) |
|
|
| Effective-mass + band-gap DFT outputs for 794 perturbed semiconductor |
| structures, plus the 156-dimensional structural fingerprint used by the |
| `crystal-rl` surrogate. Generated on AWS EC2 (`c5.4xlarge`) in February |
| 2026 over 4 rounds of bootstrap exploration starting from 10 seed |
| families (Si, Ge, C-diamond, GaAs, AlAs, InAs, GaP, SiC-3C, InP, AlN). |
|
|
| | Column | Type | Description | |
| |---|---|---| |
| | `row_id` | int64 | Stable record index (0..793). Matches the row index in the source `bootstrap_expanded_all.json`. | |
| | `seed` | string | Seed family the structure was derived from (e.g. `"Si"`, `"GaAs"`). | |
| | `label` | string | Free-form provenance label from the bootstrap pipeline. | |
| | `formula` | string | Chemical formula of the final structure (e.g. `"As2Ga2"`). | |
| | `dft_m_electron` | float64 | Signed electron effective mass at the conduction band edge (mₑ units). Negative ⇒ negative band curvature (band inversion). | |
| | `dft_m_hole` | float64 | Signed hole effective mass at the valence band edge (mₑ units). | |
| | `dft_m_min_signed` | float64 | Smallest-magnitude effective mass found in the band structure, **signed** by curvature. This is the canonical signed-m\* training target. | |
| | `dft_band_gap_ev` | float64 | PBE band gap in eV. 0 means metallic. | |
| | `dft_vbm_ev` / `dft_cbm_ev` | float64 | Valence/conduction band edge positions in eV. | |
| | `is_direct_gap` | bool? | True if Γ-point gap, False if indirect, null if undetermined. | |
| | `qe_runtime_seconds` | float64 | Wall-clock seconds the QE `pw.x` job took on a c5.4xlarge core. | |
| | `fingerprint_156` | list<float32>\[156\] | 12 composition + 4 elemental property + 8 lattice + 64 RDF + 64 partial-RDF features. Composition palette: `H, C, N, O, Si, P, Ge, Ga, As, In, Sn, Al, Sb, Bi, Se, Te`. See [`qe_interface/structures.py`](https://github.com/bshepp/crystal-rl/blob/master/qe_interface/structures.py) for the exact fingerprint code. | |
|
|
| **Signed-m\* distribution**: 681 records with `dft_m_min_signed < 0`, |
| 113 with `dft_m_min_signed > 0`. The skew toward negative is a property |
| of which structures the bootstrap perturbation chose to retain — *not* |
| of materials in general. |
|
|
| ### `surrogate_dft_pairs` (24 rows) |
|
|
| Side-by-side surrogate prediction and DFT measurement for the top |
| candidates produced by three independent RL training runs. |
|
|
| | Column | Type | Description | |
| |---|---|---| |
| | `run_id` | string | One of `phase7`, `pathb_run1`, `pathb_run3`. See [Methodology](#methodology) below for what changed between runs. | |
| | `formula` | string | Chemical formula of the candidate produced by the PPO agent. | |
| | `seed` | string | Seed structure the agent perturbed from. | |
| | `surrogate_m_star` | float64 | Surrogate's predicted effective mass for this candidate. | |
| | `surrogate_reward` | float64 | PPO reward the agent received from the surrogate. | |
| | `dft_m_min_signed` | float64 | DFT signed m\* of the minimum-magnitude band. | |
| | `dft_m_electron` | float64 | Signed conduction-band m\*. | |
| | `dft_m_hole` | float64 | Signed valence-band m\*. | |
| | `dft_band_gap_ev` | float64 | PBE band gap in eV. | |
| | `dft_converged` | bool | True if QE SCF + bands converged. (All 24 are True.) | |
| | `dft_runtime_seconds` | float64 | Wall-clock QE time. | |
| | `unusual_topology` | bool | True if DFT showed negative band curvature (band-inversion signature). | |
| | `is_low_positive_semiconductor` | bool | Convenience: `dft_m_min_signed > 0 AND < 0.5 AND dft_band_gap_ev > 0.1`. **True for 0 of 24 records.** | |
|
|
| ## Methodology |
|
|
| The `surrogate_dft_pairs` subset spans three runs of the |
| `crystal-rl` pipeline: |
|
|
| | `run_id` | Surrogate variant | What changed | Surrogate m\* corr | DFT-validated low-pos. m\* | |
| |---|---|---|---|---| |
| | `phase7` | Unsigned-m\* (pre Phase-9 fix) | Original 152-dim, 12-element palette | 0.928 | 0 / 8 | |
| | `pathb_run1` | Signed-m\*, palette expanded to 16, **bootstrap data accidentally not loaded** (fingerprint-dim mismatch with cached `surrogate_data.npz`) | 156-dim fingerprint, 16-element palette, Sb/Bi/Se/Te pseudopotentials registered | 0.495 | 0 / 8 | |
| | `pathb_run3` | Signed-m\*, palette expanded to 16, **bootstrap data loaded** (after zero-padding the cached 152-dim fingerprints to 156-dim via `scripts/regen_bootstrap_for_palette.py`) | as above | 0.768 | 0 / 8 | |
|
|
| The cumulative finding is **0 / 24** — the same answer regardless of the |
| surrogate's accuracy on its own validation set. The 24 candidates were |
| picked by three different surrogate models trained on three different |
| data mixes, but the DFT outcome is consistent: surrogate-selected |
| low-m\* predictions don't validate. |
|
|
| ## Source data & collection |
|
|
| - **`bootstrap_signed_mstar`** was collected by perturbing the 10 seed |
| crystal families (formula edits, lattice scaling, position |
| perturbation) and running PBE DFT (Quantum ESPRESSO 7.3.1, SSSP |
| Efficiency pseudopotentials, 30 Ry wavefunction cutoff, 4×4×4 k-points |
| for SCF, 20-point band path for the m\* extraction). Effective mass |
| was extracted from a parabolic fit to the band curvature at extrema |
| — sign-preserving. All 794 calculations converged. |
| - **`surrogate_dft_pairs`** was collected by running each trained PPO |
| agent for 30 deterministic episodes, deduplicating by formula, taking |
| the top 8 by surrogate-predicted reward, and DFT-validating each. All |
| 3 × 8 = 24 calculations converged. |
|
|
| ## Intended uses |
|
|
| - **Train signed-m\* surrogates** without losing the negative-curvature |
| examples. The 681 negative records are particularly load-bearing — |
| most public datasets remove the sign. |
| - **Benchmark RL-discovery pipelines** against a known failure mode. |
| If your method produces surrogate predictions that disagree |
| systematically with DFT in this same way, you're hitting the same |
| gaming dynamic. |
| - **Study the surrogate-DFT calibration gap.** The `surrogate_dft_pairs` |
| subset is small (24 rows) but each pair is a controlled |
| comparison: same fingerprint, same structure, same DFT settings, |
| different surrogate. |
| - **Cite the negative result.** This dataset is intentionally published |
| to make the finding citable rather than letting it die as a side |
| comment in a repo README. |
|
|
| ## Limitations & what this dataset is *not* |
|
|
| - This is **not a benchmark of "best low-m\* materials"** — the |
| candidate formulas listed in `surrogate_dft_pairs` were chosen by an |
| RL agent that turned out to game its surrogate. Treating them as |
| recommended materials would invert the intended use. |
| - **PBE-level DFT.** All values are PBE outputs from Quantum ESPRESSO |
| with SSSP Efficiency pseudopotentials. PBE is known to underestimate |
| band gaps and, for some compositions, give qualitatively wrong band |
| ordering. Hybrid-functional (HSE06) or GW calculations would shift |
| some values significantly. The signed-m\* *sign* is generally robust |
| to functional choice; the magnitudes are not. |
| - **Small validation cohort.** 24 surrogate-DFT pairs is a strong signal |
| for a single project but not large enough to claim a universal law |
| about RL-surrogate gaming. We report what we observed across our 3 |
| runs; we make no claim about every possible surrogate architecture. |
| - **Bootstrap structures cluster around 10 seed families.** They are |
| not a uniform sample of the periodic table. |
|
|
| ## Reproducibility |
|
|
| - **Code:** [github.com/bshepp/crystal-rl](https://github.com/bshepp/crystal-rl) |
| - **Use tag `v0.1.1` or later.** The earlier `v0.1.0` Zenodo archive |
| (`10.5281/zenodo.20362336`) is known to be incomplete — its release |
| notes describe code changes (Sb/Bi/Se/Te PSEUDO_MAP additions, |
| fingerprint palette expansion to 16 elements, `input_dim` 152→156) |
| that were not actually staged in the underlying commit. The AWS |
| runs that produced the data in this dataset used a working-tree |
| bundle that had those changes, so the **published numbers are |
| unaffected**, but anyone running `git checkout v0.1.0` will get |
| code that doesn't match the methodology. `v0.1.1` is what `v0.1.0` |
| was supposed to be. The concept DOI [`10.5281/zenodo.20362335`](https://doi.org/10.5281/zenodo.20362335) |
| always resolves to the latest version. |
| - **Pipeline:** the assembly script for this dataset is |
| [`scripts/build_hf_dataset.py`](https://github.com/bshepp/crystal-rl/blob/master/scripts/build_hf_dataset.py). |
| - **Run artifacts:** the path-B runs (`pathb_run1`, `pathb_run3`) wrote |
| full per-stage logs, surrogate weights, PPO checkpoints, and |
| manifests to a private S3 bucket; they are not redistributed here |
| because they're bulky and replicable from the code. The |
| `surrogate_dft_pairs` table is the distilled scientific output. |
| - **Quantum ESPRESSO version:** 7.3.1, built from source. SSSP |
| Efficiency pseudopotentials. |
| - **Surrogate architecture:** `MultiTaskMLP(156 → [192 × SiLU + LN + Drop] × |
| 4 → m\* head + gap head)`, ~142k parameters, trained with two-phase |
| protocol (JARVIS+bootstrap then frozen-trunk MP gap fine-tune). |
| |
| ## Citation |
|
|
| Please cite both the dataset and the underlying software: |
|
|
| ```bibtex |
| @misc{crystal_rl_surrogate_dft_gap_2026, |
| title = {Crystal-RL: surrogate-vs-DFT effective-mass gap (negative-result dataset)}, |
| author = {Sheppard, Brian}, |
| year = {2026}, |
| url = {https://huggingface.co/datasets/bshepp/rl-surrogate-dft-gap}, |
| note = {Companion dataset to github.com/bshepp/crystal-rl} |
| } |
| |
| @software{crystal_rl_2026, |
| title = {bshepp/crystal-rl: Signed effective-mass pipeline for RL-driven semiconductor discovery}, |
| author = {Sheppard, Brian}, |
| year = {2026}, |
| publisher = {Zenodo}, |
| doi = {10.5281/zenodo.20362335}, |
| url = {https://doi.org/10.5281/zenodo.20362335}, |
| note = {Concept DOI; resolves to the latest version. v0.1.0 specifically: 10.5281/zenodo.20362336} |
| } |
| ``` |
|
|
| ## License |
|
|
| CC-BY-4.0. Use freely, please cite. |
|
|
