📖 Full documentation:https://atomgptlab.github.io/alignn/

Table of Contents

• Introduction
• Installation
• Examples — train every model type
• Reproducing a JARVIS-Leaderboard contribution
• Colab notebooks
• Pre-trained models
• JARVIS-ALIGNN webapp
• ALIGNN-FF & ASE Calculator
• Peformances on a few datasets
• Useful notes
• References
• How to contribute
• Correspondence
• Funding support

ALIGNN & ALIGNN-FF (Introduction)

The Atomistic Line Graph Neural Network (paper) introduces a graph convolution layer that explicitly models both two- and three-body interactions in atomistic systems. The ALIGNN-FF variant (paper) extends this to a force-field for structurally and chemically diverse systems across 89 elements.

⚡ Pure PyTorch — DGL is no longer required. ALIGNN now runs fully in native PyTorch. Neighbor lists, line graphs, and batched readout are all built with plain torch tensor/scatter ops via alignn/torch_graph_builder.py, so you can train and run inference without installing DGL. To use the pure path, set the model name to the *_pure variant (e.g. alignn_atomwise_pure) and neighbor_strategy to "pure_torch" in your config. The example configs and tests in this repository already default to this pure-PyTorch path.

Installation

See docs/installation.md for conda, GitHub, and pip installation methods.

Examples — train every model type

All training recipes live on this page. Each one ships a self-contained, runnable example under alignn/examples/recipes/ with a make_toy_dataset.py (generates a tiny synthetic id_prop.json), a config_example.json, and its own detailed README.md. Every recipe below runs in ~1–2 minutes on CPU.

⚠️The toy datasets are smoke tests, not real models. They are 40 rattled Si cells with synthetic labels, meant only to prove the pipeline runs. For a usable model, replace the structures/labels with real DFT data (thousands → millions of entries), raise epochs to 100–300 and batch_size to 32–64, and expect to use a GPU. See each recipe's README.

Recipe	Task	Graph	Example dir
kNN	scalar property	kNN (cutoff 8)	recipes/knn
Radius	scalar property (MD-compatible)	radius (cutoff 5)	recipes/radius
Tensor	D-dim response tensor	kNN	recipes/tensor
Spectra	DOS / Raman curve	kNN	recipes/spectra
Force field	energy + forces + stress	radius	recipes/forcefield
Atomwise	per-atom charge / moment	kNN	recipes/atomwise

Every recipe reads an id_prop.json: a JSON list where each entry has a jid, an inline jarvis Atoms dict, and the target(s). See Dataset format for the full spec.

1. kNN graph — scalar property (formation energy, band gap, Tc, …)

Wider k-nearest-neighbour graph (cutoff: 8.0, max_neighbors: 12) — the more accurate choice for property prediction.

cd alignn/examples/recipes/knn
python make_toy_dataset.py # -> id_prop.json (40 toy entries)
train_alignn.py --root_dir . --config_name config_example.json \
--output_dir toy_out --target_key target --id_key jid

Key knobs: cutoff: 8.0, model.output_features: 1, graphwise_weight: 1.0, calculate_gradient: false. More: recipes/knn/README.md.

2. Radius graph — scalar property (MD-compatible neighbour list)

Same scalar task, but the fixed-radius graph (cutoff: 5.0) that is continuous under displacement — use it when you need MD-consistency.

cd alignn/examples/recipes/radius
python make_toy_dataset.py
train_alignn.py --root_dir . --config_name config_example.json \
--output_dir toy_out --target_key target --id_key jid

Key knobs: cutoff: 5.0 (vs 8.0 for kNN). More: recipes/radius/README.md.

3. Tensor property (dielectric D=9, piezo D=18, elastic D=36)

Predict a fixed-length response tensor per structure. Target is a length-D list.

cd alignn/examples/recipes/tensor
python make_toy_dataset.py
train_alignn.py --root_dir . --config_name config_example.json \
--output_dir toy_out --target_key target --id_key jid

Key knobs: set model.output_features to your tensor dimension (9/18/36) and match D in make_toy_dataset.py. More: recipes/tensor/README.md.

4. Spectra / multi-output curve (eDOS 300, pDOS 200, Raman 200)

Predict a full curve on a fixed grid. Target is a length-D list (one per bin).

cd alignn/examples/recipes/spectra
python make_toy_dataset.py
train_alignn.py --root_dir . --config_name config_example.json \
--output_dir toy_out --target_key target --id_key jid

Key knobs: model.output_features = number of bins (200/300); match D in the toy script. More: recipes/spectra/README.md.

5. Force field (energy + forces + stress, ALIGNN-FF)

Train an interatomic potential with energy-conserving (gradient) forces and stress — usable for relaxation, MD, and LAMMPS (pair_alignn).

cd alignn/examples/recipes/forcefield
python make_toy_dataset.py
train_alignn.py --root_dir . --config_name config_example.json \
--output_dir toy_out --target_key energy_per_atom --force_key forces --id_key jid

Key knobs: model.calculate_gradient: true, and the loss mixture graphwise_weight (energy) / gradwise_weight (forces) / stresswise_weight (stress). Energy must be per atom. More: recipes/forcefield/README.md.

6. Atomwise property (per-atom charges, magnetic moments)

Predict one value per atom. Target is a length-Natoms list under a per-atom key.

cd alignn/examples/recipes/atomwise
python make_toy_dataset.py
train_alignn.py --root_dir . --config_name config_example.json \
--output_dir toy_out --target_key target --id_key jid --atomwise_key charges

Key knobs: model.atomwise_output_features: 1, atomwise_weight: 1.0, graphwise_weight: 0.0; pass --atomwise_key charges. More: recipes/atomwise/README.md.

For the historical per-topic docs see also docs/training/ (dataset format, classification, multi-GPU).

Reproducing a JARVIS-Leaderboard contribution

Every ALIGNN entry on the JARVIS-Leaderboard ships the exact config, split, and run.sh used to produce it, so any result can be reproduced end to end:

# 1) install ALIGNN (pure-PyTorch, no DGL needed)
pip install alignn
# or from source:
git clone https://github.com/atomgptlab/alignn.git
cd alignn && pip install -e .&&cd ..
# 2) get the leaderboard (holds every contribution's config + data split + run.sh)
git clone https://github.com/atomgptlab/jarvis_leaderboard.git
cd jarvis_leaderboard
pip install -e .# 3) pick a contribution and re-run it# contributions live under jarvis_leaderboard/contributions/<name>/
ls jarvis_leaderboard/contributions/alignn_model/
# each folder has: the benchmark CSV, metadata.json, and run.sh
cat jarvis_leaderboard/contributions/alignn_model/run.sh
bash jarvis_leaderboard/contributions/alignn_model/run.sh

run.sh downloads the benchmark's train/val/test split (from the matching jarvis_leaderboard/benchmarks/.../*.json.zip), writes the id_prop/config, and calls train_alignn.py with the same settings that produced the leaderboard number — so you reproduce the published MAE exactly. To submit a new ALIGNN result, copy an existing contribution folder, drop in your predictions CSV + metadata.json, and open a PR (see the leaderboard's CONTRIBUTING).

Colab notebooks

Ready-to-run notebooks covering property prediction, force-field training, and pretrained-model usage. Click a badge to open in Colab.

Notebook	Open in Colab	Description
Regression task (graph-wise prediction)		Single-output regression for 2D-material exfoliation energies.
ML force-field training from scratch		Train an ALIGNN-FF force field for Silicon.
ALIGNN-FF: relaxation, EV curve, phonons, interfaces		Pretrained ALIGNN-FF for relaxation, EV curves, phonons, and interfaces.
Scaling / timing comparison		Scaling/timing analysis of universal MLFFs.
Melt-Quench MD		Generate amorphous structures via molecular dynamics.
Miscellaneous training tasks		Single-output, multi-output (phonon/electron DOS), classification, and pretrained usage.
Superconductor Tc		Train a model for superconductor transition temperature.
Build id_prop.json from VASP runs		Compile vasprun.xml files into id_prop.json for ALIGNN-FF training.
LAMMPS MD with ALIGNN-FF (pair_alignn)		Build LAMMPS with the native pair_alignn style and run NVE / melt-quench MD with the default ALIGNN-FF mps force field.

Using pre-trained models

See docs/pretrained/:

• Property predictor
• ALIGNN-FF

Web-apps

See docs/usage/webapps.md. Direct links: AtomGPT ALIGNN app, ALIGNN-FF app.

ALIGNN-FF ASE Calculator

fromase.buildimportbulkfromalignn.ff.unified_calculatorimport (
AlignnUnifiedCalculator, AlignnUnifiedConfig)
cfg=AlignnUnifiedConfig(
energy=True, forces=True, stress=True,
properties=["formation_energy_peratom", "optb88vdw_bandgap"],
)
calc=AlignnUnifiedCalculator(cfg) # models loaded once, reusedsi=bulk("Si", "diamond", a=5.43); si.calc=calcsi.get_potential_energy(); si.get_forces(); si.get_stress()
print(calc.predictions()) # extra property predictors

A single pydantic config selects the outputs (force-field energy/forces/stress plus any pretrained ALIGNN 2.0 property predictors — scalar, spectra, or D-dim tensor; radius or kNN graph). See docs/usage/ase-calculator.md for more, and the ASE docs page Calculators → ALIGNN.

Performances

ALIGNN 2.0 benchmarked across single-property, multi-property (spectra / per-atom / tensor), and interatomic-force-field tasks. Columns compare ALIGNN 2.0 on the radius and 8 Å kNN graphs against the original ALIGNN and CGCNN; bold marks the row best. Skill is 100 · (1 − MAE / MAD) vs the mean-absolute-deviation baseline. For the live, continually-updated numbers see the JARVIS-Leaderboard.

Full benchmark table (54 tasks)

(a) Single-property prediction — test MAE

#	Task (unit)	N tr/val/te	ALIGNN 2.0 (radius)	ALIGNN 2.0 (kNN)	orig. ALIGNN	CGCNN	Baseline (MAD)	Skill %
1	formation_energy (eV/atom)	44569/5572/5572	0.0316	0.0307	0.0331	0.0551	0.876	96.5
2	optb88vdw_total_energy (eV/atom)	44569/5572/5572	0.0321	0.0314	0.0367	0.0584	1.786	98.2
3	optb88vdw_bandgap (eV)	44569/5572/5572	0.1314	0.1306	0.1423	0.1857	0.999	86.9
4	mbj_bandgap (eV)	14535/1817/1815	0.2721	0.2730	0.3104	0.3261	1.765	84.6
5	QM9 HOMO–LUMO gap (eV)	110,000/10,000/10,829	0.031		0.0345		0.834	96.3
6	QMOF bandgap (eV)	16,340/2042/2042	0.208		0.202		0.946	78.7
7	ehull (eV/atom)	44290/5537/5537	0.0576	0.0590	0.0763	0.0590	1.148	95.0
8	bulk_modulus_kv (GPa)	15744/1968/1968	9.885	9.302	10.399	11.015	53.76	82.7
9	shear_modulus_gv (GPa)	15744/1968/1968	9.063	8.825	9.476	10.079	27.06	67.4
10	magmom_oszicar (μ_B)	41766/5222/5222	0.2608	0.2567	0.2574	0.3065	1.254	79.5
11	slme (%)	7250/906/906	4.493	4.447	4.521	5.014	11.21	60.3
12	spillage	9101/1137/1137	0.3527	0.3456	0.3510	0.3844	0.518	33.3
13	kpoint_length_unit (Å)	44313/5540/5539	9.699	9.342	9.515	9.875	17.94	47.9
14	encut (eV)	44308/5539/5539	131.81	128.08	133.80	134.83	262.6	51.2
15	epsx	35592/4449/4449	20.705	20.139	20.394	22.199	57.45	64.9
16	epsy	35592/4449/4449	20.088	19.829	19.999	21.787	57.32	65.4
17	epsz	35592/4449/4449	19.633	19.453	19.568	21.121	55.79	65.1
18	mepsx	13447/1681/1681	24.646	23.847	24.046	26.929	63.39	62.4
19	mepsy	13447/1681/1681	23.823	24.044	23.648	26.556	63.68	62.6
20	mepsz	13447/1681/1681	23.247	23.531	23.731	26.629	60.71	61.7
21	dfpt_piezo_max_dij (pC/N)	2677/334/334	12.603	12.498	20.570	18.392	22.69	44.9
22	dfpt_piezo_max_dielectric	3764/470/470	26.823	24.305	28.151	30.961	43.91	44.7
23	exfoliation_energy (meV/atom)	650/81/81	40.272	37.628	52.703	45.762	61.03	38.3
24	max_efg (10²¹V/m^2)	9493/1186/1186	19.802	19.248	19.121	22.957	44.46	56.7
25	avg_elec_mass (m_e)	14114/1764/1764	0.0837	0.0810	0.0853	0.0921	0.225	64.1
26	avg_hole_mass (m_e)	14114/1764/1764	0.1299	0.1240	0.1239	0.1406	0.399	68.9
27	n_Seebeck (\muV/K)	18568/2321/2321	41.524	40.346	40.921	45.660	111.5	63.8
28	n_powerfact (\muW/mK^2)	18568/2321/2321	469.07	451.90	442.30	485.59	709.2	36.3
29	ph_heat_capacity (J/mol/K)	9644/1205/1205	9.577	–	9.606	12.936	40.16	76.2
30	Thermal Cond. (log₁₀κ_L)	3227/–/404	0.376	0.362	–	–	0.597	39.4
31	Tc_supercon (K)	556/30/30	1.637	1.490	2.032	–	2.723	45.3
32	Tc_supercon_hydride (K)	763/95/95	9.937	9.425	–	–	33.56	71.9
33	Tc_supercon_ hydride_plus_bulk (K)	1595/199/199	8.670	8.407	–	–	22.33	62.3
34	alex_supercon Tc (K)	6592/824/825	0.883				2.818	68.7
35	alex_supercon N(E_F) (states/eV)	6592/824/825	0.821				1.559	47.3
36	alex_supercon θ_D (K)	6592/824/825	11.33				80.30	85.9
37	alex_supercon λ	6592/824/825	0.0707				0.194	63.6
38	alex_supercon ω_log (K)	6592/824/825	20.31				55.37	63.3

(b) Multi-property — spectra / per-atom / tensor; held-out MAE (col. "radius")

#	Task (unit)	N tr/val/te	ALIGNN 2.0 (radius)	ALIGNN 2.0 (kNN)	orig. ALIGNN	CGCNN	Baseline (MAD)	Skill %
39	eDOS, electronic DOS (D=300)	4103/227/229	0.0138				0.0213	35.2
40	pDOS, phonon DOS (D=200)	4103/227/229	0.0819				0.117	29.8
41	Raman spectrum (D=200)	4059/507/508	0.0378	0.0326			0.0497	34.4
42	Bader charge, per atom (e)	75,028/3000/3000	0.0192				2.124	99.1
43	Net charge, per atom (e)	75,033/3000/3000	0.0167				–	–
44	Magnetic moment, per atom (μ_B)	89,231/3000/3000	0.0256				2.063	98.8
45	Dielectric tensor (D=9)	4103/227/229	1.690				3.401	50.3
46	Born effective charge (e)	4472/248/249	0.234				–	–
47	Piezoelectric tensor, C/m^2 (D=18)	4513/250/252	0.077				0.089	13.9
48	Elastic C_{ij} tensor, GPa (D=36)	15,936/885/886	5.593				18.73	70.1

(c) Interatomic force fields — mlearn per-element energy/force; large sets energy / force

#	Task (unit)	N tr/val/te	ALIGNN 2.0 (radius)	ALIGNN 2.0 (kNN)	orig. ALIGNN	CGCNN	Baseline (MAD)	Skill %
49	mlearn-Si, energy (meV/atom)	214/–/25	13.88‡				–
50	mlearn-Si, force (eV/Å)	214/–/25	0.0872‡				–
51	ALIGNN-FF-DB (E/F)	276,401/–/15,355	32.4† / 0.0564†				–
52	MATPES-PBE (E/F)	391,241/21,736/–	40.4 / 0.1475				–
53	FD-FF, 1.1 M (E/F)	1,097,227/60,957/60,958	28.9† / 0.0445†				–
54	MPtrj (E/F)	~1.5 M	56.7† / 0.0707†				–
*Blank cells: not run for that graph/model. –: baseline unavailable or ill-defined.
† still training. ‡ mlearn MAE pending re-verification against a consistent per-atom
energy convention.*

Useful notes

Tips & FAQ

Pure-PyTorch path (no DGL)

• ALIGNN 2.0 runs fully in native PyTorch — set the model name to a *_pure variant (e.g. alignn_atomwise_pure) and neighbor_strategy: "pure_torch". DGL is optional.
• If you do use the legacy DGL path, install a DGL build matching your CUDA runtime; mismatched builds are the most common install failure.

Structure file parsing

• Simple .cif/.pdb are handled by jarvis-tools directly.
• For complex CIFs: pip install cif2cell==2.0.0a3. For complex PDBs: conda install -c ambermd pytraj.

Training hyperparameters

• Example configs ship with a small batch_size/epochs so tests run fast. Use batch_size: 32–64 and epochs: 100–300 for real trainings — otherwise training is slow and under-performing.
• pandas >= 1.2.3 required. Since March 2024, pytorch-ignite is no longer a dependency.

CLIs are importable scripts

• train_alignn.py, pretrained.py, and run_alignn_ff.py install as executables in your environment's bin/ — just run them by name, no absolute path needed.

Known dataset issues

• QM9: see issue #54 for a data-split discrepancy affecting reproducibility.

Getting help

• GitHub issues: https://github.com/atomgptlab/alignn/issues · Email: drkamal@jhu.edu

References

If ALIGNN or ALIGNN-FF contributed to your work, please cite the relevant papers.

Publication list

Core

1. Choudhary, K. & DeCost, B. Atomistic Line Graph Neural Network for improved materials property predictions.npj Computational Materials 7, 185 (2021). Link
2. Choudhary, K., DeCost, B., Major, L., Butler, K., Thiyagalingam, J., Tavazza, F. Unified graph neural network force-field for the periodic table.Digital Discovery (2023). Link

Applications

3. Prediction of the Electron Density of States for Crystalline Compounds with ALIGNN.Link
4. Recent advances and applications of deep learning methods in materials science.Link
5. Designing High-Tc Superconductors with BCS-inspired Screening, DFT, and Deep-learning.Link
6. A Deep-learning Model for Fast Prediction of Vacancy Formation in Diverse Materials.Link
7. Graph neural network predictions of MOF CO₂ adsorption properties.Link
8. Rapid Prediction of Phonon Structure and Properties using ALIGNN.Link
9. Large Scale Benchmark of Materials Design Methods.Link
10. Prediction of Magnetic Properties in van der Waals Magnets using GNNs.Link
11. CHIPS-FF: Benchmarking universal force-fields.Link

A complete list is maintained at jarvis-tools publications.

How to contribute

See Contribution instructions and docs/contributing.md.

Correspondence

Please report bugs as GitHub issues or email drkamal@jhu.edu.

Funding support

• NIST-MGI
• NIST-CHIPS

Code of conduct

Please see Code of conduct.