MaterialsSaddles

A high-throughput library of converged transition states for solid-state and surface chemistry. Hub URL: https://huggingface.co/datasets/AnonymouScientist/MaterialsSaddles

34,135,597 fully converged transition states computed by massively-parallel saddle searches on top of public materials and catalysis datasets, using the SaddleMill package and Meta's uma-s-1p2 machine-learning interatomic potential.

Each entry in a file is a single structure. Three consecutive entries form one transition-state event: reactant minimum, transition state (first-order saddle), product minimum. Endpoints are converged to 0.02 eV/Å (max|F|), saddles to 0.05 eV/Å. Each saddle row also stores its eigenmode — an (N, 3) per-atom displacement field giving the direction along which the saddle is unstable.

---

Quick stats

Transition states (total)	34,135,597
Files (.aselmdb)	416
Rows per TS	3 (reactant, saddle, product), in order
Saddle search method	Dimer (lemat / oc20 / oc22), NEB-CI (mp20bat)
Calculator	fairchem uma-s-1p2
Endpoint convergence (max|F|)	0.02 eV/Å
Saddle convergence (max|F|)	0.05 eV/Å
License	CC-BY-4.0

Breakdown by source dataset

Subset	Source	Method	# transition states	Files
lemat/	LeMat-Bulk	Dimer	31,346,419	256
oc20/	Open Catalyst 2020 (OC20)	Dimer	2,587,101	96
oc22/	Open Catalyst 2022 (OC22)	Dimer	167,335	32
mp20bat/	Materials Project battery structures	NEB-CI	34,742	32

---

Try it: minimal example notebook

A self-contained Jupyter notebook (example.ipynb) demonstrates loading the dataset, converting ASE-LMDB rows to ASE Atoms objects (including a non-obvious atoms.info round-trip), walking the (R, S, P) triplet layout, visualizing a reaction, loading the train/val/test split manifests, and reproducing two small panels of Fig. 1 of the accompanying paper. It auto-installs its dependencies and downloads roughly 1 GB of sample shards (one each from lemat, oc20, oc22, and mp20bat); end-to-end runtime is roughly five minutes on a typical laptop connection.

To run locally:

bash hf download AnonymouScientist/MaterialsSaddles example.ipynb \ --repo-type dataset --local-dir . jupyter notebook example.ipynb

You can also open the notebook directly in the Hugging Face web UI; it renders inline.

---

Directory structure

.
├── README.md                          (this file)
├── DATASHEET.md                       (Datasheet for Datasets, Gebru et al. 2018)
├── lemat/
│   ├── lemat_dimer_000.aselmdb
│   ├── ...
│   └── lemat_dimer_255.aselmdb        (256 files)
├── mp20bat/
│   ├── mp20bat_neb_000.aselmdb
│   ├── ...
│   └── mp20bat_neb_031.aselmdb        (32 files)
├── oc20/
│   ├── oc20_dimer_000.aselmdb
│   ├── ...
│   └── oc20_dimer_095.aselmdb         (96 files)
└── oc22/
    ├── oc22_dimer_000.aselmdb
    ├── ...
    └── oc22_dimer_031.aselmdb         (32 files)

The split into multiple .aselmdb shards is purely for file-size convenience (each shard is at most a few GB). Shards within a subset are interchangeable and can be processed in any order.

---

What is in each row?

Each .aselmdb is an ASE-LMDB database whose rows are stored in triplets:

row 0  reactant  (Dimer:  side = -1   ;  NEB:  image_type = 'endpoint')
row 1  saddle    (Dimer:  side =  0   ;  NEB:  image_type = 'climbing')   ← TS
row 2  product   (Dimer:  side =  1   ;  NEB:  image_type = 'endpoint')
row 3  reactant
...

The rich per-row metadata lives in row.data['info']. After row.toatoms(), atoms.info is empty — you have to copy row.data['info'] over yourself (see Loading).

Each row also exposes a small set of searchable scalar key/value pairs via row.key_value_pairs — at minimum task_name and ms_id, plus src_index, side, and status where applicable. These are convenient for db.select(...)-style filtering, but note that ASE's aselmdb backend performs linear scans: queries are O(N) per shard, not indexed.

The keys in row.data['info'] vary by source dataset and saddle-search method. Only task_name and ms_id are guaranteed on every row — for anything else, the table below documents which subsets typically have it. When in doubt, inspect row.data['info'].keys() for a few rows of your target subset.

key (in info dict)	who has it	what it is
side	dimer rows	-1 / 0 / 1 (reactant / saddle / product)
image_type	NEB rows	'endpoint' (reactant/product) or 'climbing' (saddle)
image_idx, subband_idx, nimages	NEB rows	NEB band geometry
image_converged, band_converged, band_converged_CI	NEB rows	per-image / band-level convergence flags
effective_fmax	NEB rows	per-image max-force (NEB-modified) at convergence
converged	dimer endpoint rows	endpoint converged flag
eigenmode	saddle rows	(N, 3) float64 — the eigenmode at the saddle, one 3D displacement vector per atom
curvature	dimer saddles	eigenvalue along that eigenmode (eV/Ų)
barrier, dE	NEB saddles	reactant→TS and reactant→product energy differences (eV)
is_reaction, n_formed_bonds, n_broken_bonds, formed_bonds, broken_bonds	dimer rows	bond changes detected via ASE neighbor lists
is_ads_reaction, n_ads_*, ads_*_bonds	dimer rows	adsorbate-restricted bond changes
parent_ts_index	dimer endpoints	upstream SaddleMill identifier of the parent saddle (an internal pointer; the saddle in the same triplet is the immediately adjacent row in this .aselmdb file)
src_index	all rows	SaddleMill internal ID (file-local)
ms_id	all rows	global, contiguous row index across the entire dataset (0-indexed, 0..102,406,790). Unique across all 416 shards and all four subsets. Triplets occupy three consecutive ms_ids: 3k (reactant), 3k+1 (saddle), 3k+2 (product). Used by the splits/ manifests below.
task_name	all rows	UMA task head used during the saddle search. Varies by subset: "omat" for lemat and mp20bat, "oc20" for oc20, "oc22" for oc22.
status	all rows	SaddleMill run status (always a converged* value here)
orig_info	all rows	nested dict; carries the source-dataset identifiers (see below)

row.data may carry additional internal bookkeeping fields (e.g. traj_path) that are artifacts of the upstream production pipeline and have no scientific or downstream value. Read from row.data['info']; ignore anything else.

Where to find the source-dataset identifiers

The location depends on whether the input went through one or two stages of the SaddleMill pipeline before this release:

Subset	Path to source IDs in the row's info dict	Example fields
lemat	info['orig_info']['orig_info']	immutable_id (e.g. 'agm005964602'), chemical_formula_*, functional, entalpic_fingerprint
oc20	info['orig_info']['orig_info']	source_file (e.g. 'random1176828.extxyz.xz')
oc22	info['orig_info']['orig_info']	sid, id, nads, natoms
mp20bat	info['orig_info']	discharge_id / charge_id (Materials Project IDs, e.g. 'mp-1006112'), working_ion, removed_ion_idxs

For Dimer subsets info['orig_info'] itself is a SaddleMill-internal dict (attempt_id, reaction_type, etc.) and the upstream identifiers live one level deeper. For the NEB subset there's only one level of nesting.

---

Intended uses

This dataset was built with three downstream uses in mind:

1. Training generative models for transition-state prediction. Each triplet gives reactant + product (conditioning) and saddle (target). The eigenmode and bond-change annotations make it easy to filter for chemically meaningful events.
2. Generating DFT labels to fight MLIP barrier softening. ML interatomic potentials systematically under-predict activation barriers. Computing single-point energies/forces on these saddles + endpoints with DFT yields targeted training data that pushes MLIPs toward correct barrier heights without requiring full DFT saddle searches.
3. Warm-starting DFT saddle searches. The ML-relaxed saddles are usually close enough to the DFT minimum that running a Dimer/NEB at DFT level converges in a small number of force evaluations.

---

Loading the data

Requirements

pip install "ase>=3.26.0" ase_db_backends

ase_db_backends registers the aselmdb backend so ase.db.connect(path, type="aselmdb") works directly. No fairchem-core install is required to read the data. If you happen to have fairchem-core in your environment already, an import fairchem.core.datasets before connect works as a fallback registration on older stacks.

⚠ The atoms.info reconstruction trap

ASE's aselmdb backend does not round-trip atoms.info. Calling row.toatoms() returns an Atoms object whose .info is empty — the full original info dict (every metadata key documented above, including nested orig_info and the eigenmode ndarray) lives in row.data["info"]. Always use the canonical reader helper:

def row_to_atoms(row):
    atoms = row.toatoms()
    atoms.info.update(row.data["info"])  # restore the original info dict
    return atoms

Minimal example

from ase.db import connect

db = connect("lemat/lemat_dimer_000.aselmdb", type="aselmdb")
for row in db.select(limit=10):
    atoms = row_to_atoms(row)
    print(atoms.get_chemical_formula(),
          "side=", atoms.info.get("side"),
          "image_type=", atoms.info.get("image_type"))

Walking the rows in triplets

from ase.db import connect

db = connect("lemat/lemat_dimer_000.aselmdb", type="aselmdb")
print(len(db), "rows ->", len(db) // 3, "transition states")

batch = []
for row in db.select():
    batch.append(row_to_atoms(row))
    if len(batch) == 3:
        reactant, saddle, product = batch
        print(saddle.get_chemical_formula(),
              "eigenmode", saddle.info.get("eigenmode").shape if "eigenmode" in saddle.info else None,
              "curvature", saddle.info.get("curvature"),
              "barrier",   saddle.info.get("barrier"))
        batch = []

The same loop works without modification on every subset (lemat, oc20, oc22, mp20bat).

---

Train / val / test splits

A precomputed stratified 90 / 5 / 5 train / val / test split is published alongside the data under splits/. The split is:

• Stratified by subset — each of lemat, oc20, oc22, mp20bat is independently split 90 / 5 / 5, so the global split preserves subset proportions exactly.
• Triplet-level — the three rows of a transition-state event (reactant, saddle, product) always land in the same split.
• Deterministic — produced with NumPy seed 42 from the global ms_id enumeration. Re-running the build script reproduces the same assignment.

Layout

splits/
├── lemat/    train.parquet  val.parquet  test.parquet
├── oc20/     train.parquet  val.parquet  test.parquet
├── oc22/     train.parquet  val.parquet  test.parquet
└── mp20bat/  train.parquet  val.parquet  test.parquet

Each parquet file has a single ms_id (uint32) column listing all ms_ids that belong to that (subset, split) bucket, sorted ascending. Every triplet contributes its three consecutive ms_ids (3k, 3k+1, 3k+2).

Counts

subset	total	train	val	test
lemat	31,346,419	28,211,777	1,567,321	1,567,321
oc20	2,587,101	2,328,391	129,355	129,355
oc22	167,335	150,602	8,367	8,366
mp20bat	34,742	31,268	1,737	1,737

Using the splits

Train on a single subset:

import polars as pl
train_ms_ids = pl.read_parquet("splits/lemat/train.parquet")["ms_id"].to_numpy()

Train on the union of all subsets:

import polars as pl
train_ms_ids = pl.concat([
    pl.read_parquet(f"splits/{s}/train.parquet")
    for s in ("lemat", "oc20", "oc22", "mp20bat")
])["ms_id"].to_numpy()

Route rows from an aselmdb shard at iteration time. Because each parquet column is sorted, np.searchsorted is the fastest membership test:

import numpy as np
from ase.db import connect
import fairchem.core.datasets  # registers aselmdb backend

train_ms_ids = ...  # loaded as above; sorted uint32 array
db = connect("lemat/lemat_dimer_000.aselmdb", type="aselmdb")
for row in db.select():
    ms_id = row.data["info"]["ms_id"]
    idx = np.searchsorted(train_ms_ids, ms_id)
    if idx < len(train_ms_ids) and train_ms_ids[idx] == ms_id:
        # row is in the training split — feed it to your trainer
        ...

The same parquet files can be loaded with datasets:

from datasets import load_dataset
ds = load_dataset(
    "AnonymouScientist/MaterialsSaddles",
    data_files={"train": "splits/lemat/train.parquet",
                "val":   "splits/lemat/val.parquet",
                "test":  "splits/lemat/test.parquet"},
)

Reproducing the split

The 12 parquet files are produced deterministically: shuffle each subset's triplet indices with NumPy seed 42, take the first 90% as train, next 5% as val, last 5% as test, then expand each triplet t to its three consecutive ms_ids {3t, 3t+1, 3t+2}. The per-subset triplet counts in the table above are sufficient to regenerate every file under splits/ byte-identically.

---

How the data was produced

We took fully relaxed structures from four public datasets (LeMat-Bulk, OC20, OC22, and Materials Project battery structures) and ran high-throughput saddle searches against each one using the SaddleMill package, with Meta's uma-s-1p2 universal interatomic potential (fairchem-core) as the calculator.

Subset	Method	SaddleMill entrypoint
lemat	Dimer	SaddleMill.dimeropt
oc20	Dimer	SaddleMill.dimeropt
oc22	Dimer	SaddleMill.dimeropt
mp20bat	NEB-CI	SaddleMill.nebopt (climbing image)

Initialization protocol (per-subset displacement modes such as vacancy, hop_insert, kickout_*, ring, adsorbate_atom, diffusion, rotation, …), eigenmode refinement, and post-search filtering are documented in the companion paper.

After saddle convergence, every TS was validated by DoubleMinimization — displacing along the eigenmode in both directions and relaxing — and only triplets where the resulting endpoints actually correspond to two distinct basins (i.e. a real reaction occurred) are kept here. Anything that errored, hit a step limit, desorbed, or failed the reaction check is excluded.

Convergence rate of the saddle searches

The fraction of attempted saddle searches that converged on a real reaction (and thus appear in this release):

Subset	Search attempts	Released TS	Convergence rate
lemat	TODO	31,346,419	TODO
oc20	TODO	2,587,101	TODO
oc22	TODO	167,335	TODO
mp20bat	TODO	34,742	TODO

Numbers to be filled in.

---

Known limitations

• MLIP, not DFT. All saddles and endpoints in this release were converged with the uma-s-1p2 MLIP rather than DFT. ML interatomic potentials systematically under-predict activation barriers, so the geometries here should be treated as approximate transition states. For DFT-level accuracy, run a single-point or short DFT saddle/NEB starting from these structures.
• atoms.info is not auto-restored by row.toatoms(). See the trap callout above. Always use the row_to_atoms helper.
• row.key_value_pairs queries are linear scans. ASE's aselmdb backend has no secondary indices; db.select(side=0) reads every row. For large filters, iterate the rows yourself in shard order.
• Multi-shard cursors are user code. Each shard is independent; if you need to iterate the entire subset (or use the splits/ manifests), open shards in sequence and route rows by row.data["info"]["ms_id"].
• Schema varies by source. Only task_name and ms_id are guaranteed on every row. NEB-derived rows (e.g. mp20bat) have a different info schema than dimer rows (e.g. no side, but image_type / image_idx / barrier instead). Inspect row.data["info"].keys() if you need to discover what's actually there for a given subset.

---

Citation

If you use this dataset, please cite:

@article{TODO_OUR_PAPER,
  title   = {{TODO: paper title}},
  author  = {{TODO: authors}},
  journal = {{TODO: venue}},
  year    = {{TODO: year}},
  note    = {{TODO: link / arXiv ID}}
}

…and the upstream sources you actually used:

@article{chanussot2021oc20,
  title   = {Open Catalyst 2020 (OC20) Dataset and Community Challenges},
  author  = {Chanussot, Lowik and Das, Abhishek and Goyal, Siddharth and others},
  journal = {ACS Catalysis},
  volume  = {11},
  pages   = {6059--6072},
  year    = {2021},
  doi     = {10.1021/acscatal.0c04525}
}

@article{tran2023oc22,
  title   = {The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts},
  author  = {Tran, Richard and Lan, Janice and Shuaibi, Muhammed and others},
  journal = {ACS Catalysis},
  volume  = {13},
  pages   = {3066--3084},
  year    = {2023},
  doi     = {10.1021/acscatal.2c05426}
}

@article{jain2013mp,
  title   = {Commentary: The {Materials Project}: A materials genome approach to accelerating materials innovation},
  author  = {Jain, Anubhav and Ong, Shyue Ping and Hautier, Geoffroy and others},
  journal = {APL Materials},
  volume  = {1},
  number  = {1},
  pages   = {011002},
  year    = {2013},
  doi     = {10.1063/1.4812323}
}

@misc{lemat-bulk,
  title  = {{LeMat-Bulk}: A unified, deduplicated dataset of bulk crystal structures},
  author = {{Entalpic} and {Hugging Face}},
  year   = {2024},
  note   = {\url{https://huggingface.co/datasets/LeMaterial/LeMat-Bulk}}
}

@misc{uma2025,
  title  = {{UMA}: A Family of Universal Models for Atoms},
  author = {{Meta FAIR Chemistry}},
  year   = {2025},
  note   = {\url{https://github.com/facebookresearch/fairchem} -- model {\tt uma-s-1p2}}
}

@article{ase,
  title   = {The atomic simulation environment---a {Python} library for working with atoms},
  author  = {Larsen, Ask Hjorth and Mortensen, Jens J{\o}rgen and Blomqvist, Jakob and others},
  journal = {Journal of Physics: Condensed Matter},
  volume  = {29},
  pages   = {273002},
  year    = {2017},
  doi     = {10.1088/1361-648X/aa680e}
}

Several of the entries above are placeholders or trimmed; please verify the canonical version against the publisher before submission.

License

This dataset is released under Creative Commons Attribution 4.0 International (CC-BY-4.0).

The upstream datasets retain their own licenses; consult them before any redistribution that combines this dataset with theirs.

Changelog

• v1 — initial public release.

Contact

Issues / questions: open a discussion on the Hugging Face Hub page.