--- 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.