neighborlist-rs 0.1.9

High-performance neighborlist construction for atomistic systems in Rust with Python bindings.

neighborlist-rs

High-performance neighborlist construction for atomistic systems in Rust with Python bindings.

Features

• High-performance Cell Lists: O(N) scaling for large systems.
• Batched Processing: Parallelized neighbor list construction across multiple systems (ideal for GNN training).
• Multi-Cutoff pass: Generate multiple neighbor lists (e.g., short-range GNN, long-range dispersion) in a single optimized pass.
• SIMD Acceleration: Explicit wide SIMD kernels for small systems and brute-force fallbacks.
• Auto-Box Inference: Automatic bounding box calculation for isolated molecules.
• Robust PBC support: Minimum image convention for orthorhombic and triclinic cells.
• ASE Integration: Direct support for ase.Atoms objects.
• Zero-copy NumPy integration: Efficient data exchange via PyO3 and rust-numpy.

Installation

From PyPI

pip install neighborlist-rs

From Source

pip install .

Usage (Python)

Refer to PYTHON_API.md for full documentation and advanced usage examples.

Quick Start (with ASE)

import neighborlist_rs
from ase.build import molecule

atoms = molecule("C2H5OH")
# Single pass search directly from ASE Atoms
result = neighborlist_rs.build_from_ase(atoms, cutoff=5.0)

edge_index = result["edge_index"]  # (2, M) array of atom pairs
shifts = result["shift"]           # (M, 3) periodic shift vectors

Manual Search

import neighborlist_rs
import numpy as np

positions = np.array([[0, 0, 0], [1, 1, 1]], dtype=float)
# None for cell means isolated system (non-PBC)
result = neighborlist_rs.build_neighborlists(None, positions, cutoff=5.0)

Usage (Rust)

Refer to RUST_API.md for detailed Rust documentation.

Single System Search

use neighborlist_rs::build_neighborlists;

let positions = [[0.0, 0.0, 0.0], [1.0, 0.0, 0.0]];
let cutoff = 1.5;
let result = build_neighborlists(&positions, cutoff, None, true).unwrap();

Performance

neighborlist-rs is designed for high-performance atomistic simulations, outperforming established baselines across a wide range of system sizes and conditions.

Comprehensive Benchmarks

(Measured on CPU: ARM64 8-core / NVIDIA GB10 proxy. Results in wall-clock time ms.)

1. Single System Scaling

The table below shows the average wall-clock time (in milliseconds) to construct the neighbor list. Lower is better.

System	Cutoff	ASE	Matscipy	Freud	Vesin	neighborlist-rs
Isolated 1k	6.0 Å	101.5	3.3	4.1	1.7	3.0
	14.0 Å	2688.1	10.2	12.2	5.8	2.8
Ethanol 1k	6.0 Å	149.9	5.9	2.7	3.0	5.3
(PBC)	14.0 Å	6677.0	130.8	N/A*	64.0	37.6
Ethanol 10k	6.0 Å	1222.8	50.7	34.9	37.9	19.8
(PBC)	14.0 Å	11948.5	608.5	485.1	342.6	250.5

*Freud requires box dimensions > 2x cutoff.

Detailed Raw Timing Summary

======================================== Single System Benchmarks ========================================

>>> CUTOFF: 6.0 Angstroms <<<

--- Isolated 1k (N=1000, rc=6.0) ---
Library              | Device | Time (ms)  | Std       
-------------------------------------------------------
 ASE                 | CPU   | 101.48     | 0.76      
 Matscipy            | CPU   | 3.28       | 0.01      
 Freud               | CPU   | 4.13       | 0.85      
 Vesin               | CPU   | 1.72       | 0.02      
 RS (1 thread)       | CPU   | 3.16       | 0.47      
 RS (Parallel)       | CPU   | 2.97       | 0.26      
 TorchCluster        | CPU   | 2.49       | 0.11      
 TorchCluster        | GPU   | 18.51      | 1.05      
 TorchNL             | CPU   | 16.34      | 3.01      
 TorchNL             | GPU   | 6.52       | 0.80      

--- Ethanol 1k (PBC) (N=1008, rc=6.0) ---
Library              | Device | Time (ms)  | Std       
-------------------------------------------------------
 ASE                 | CPU   | 149.92     | 15.19     
 Matscipy            | CPU   | 5.91       | 0.01      
 Freud               | CPU   | 2.71       | 0.59      
 Vesin               | CPU   | 2.99       | 0.02      
 RS (1 thread)       | CPU   | 5.44       | 0.11      
 RS (Parallel)       | CPU   | 5.35       | 0.01      
 TorchCluster        | ---   | N/A (No PBC support) |           
 TorchNL             | CPU   | 31.72      | 1.27      
 TorchNL             | GPU   | 18.85      | 4.69      

--- Ethanol 10k (PBC) (N=10008, rc=6.0) ---
Library              | Device | Time (ms)  | Std       
-------------------------------------------------------
 ASE                 | CPU   | 1222.77    | 2.29      
 Matscipy            | CPU   | 50.70      | 0.21      
 Freud               | CPU   | 34.93      | 2.36      
 Vesin               | CPU   | 37.92      | 0.56      
 RS (1 thread)       | CPU   | 20.61      | 0.68      
 RS (Parallel)       | CPU   | 19.82      | 0.10      
 TorchCluster        | ---   | N/A (No PBC support) |           
 TorchNL             | CPU   | 487.77     | 8.08      
 TorchNL             | GPU   | 134.06     | 27.62     


>>> CUTOFF: 14.0 Angstroms <<<

--- Isolated 1k (N=1000, rc=14.0) ---
Library              | Device | Time (ms)  | Std       
-------------------------------------------------------
 ASE                 | CPU   | 2688.05    | 5.74      
 Matscipy            | CPU   | 10.16      | 0.03      
 Freud               | CPU   | 12.20      | 2.00      
 Vesin               | CPU   | 5.82       | 0.06      
 RS (1 thread)       | CPU   | 2.89       | 0.07      
 RS (Parallel)       | CPU   | 2.82       | 0.06      
 TorchCluster        | CPU   | 9.14       | 0.51      
 TorchCluster        | GPU   | 18.36      | 0.82      
 TorchNL             | CPU   | 81.01      | 4.50      
 TorchNL             | GPU   | 24.08      | 1.98      

--- Ethanol 1k (PBC) (N=1008, rc=14.0) ---
Library              | Device | Time (ms)  | Std       
-------------------------------------------------------
 ASE                 | CPU   | 6676.95    | 4.42      
 Matscipy            | CPU   | 130.84     | 0.63      
 Vesin               | CPU   | 63.95      | 0.47      
 RS (1 thread)       | CPU   | 37.62      | 0.22      
 RS (Parallel)       | CPU   | 37.88      | 0.15      
 TorchCluster        | ---   | N/A (No PBC support) |           
 TorchNL             | CPU   | 522.08     | 8.28      
 TorchNL             | GPU   | 90.39      | 2.55      

--- Ethanol 10k (PBC) (N=10008, rc=14.0) ---
Library              | Device | Time (ms)  | Std       
-------------------------------------------------------
 ASE                 | CPU   | 11948.54   | 273.39    
 Matscipy            | CPU   | 608.45     | 2.64      
 Freud               | CPU   | 485.05     | 3.00      
 Vesin               | CPU   | 342.63     | 1.84      
 RS (1 thread)       | CPU   | 250.45     | 5.98      
 RS (Parallel)       | CPU   | 264.32     | 3.57      
 TorchCluster        | ---   | N/A (No PBC support) |           
 TorchNL             | CPU   | 4605.02    | 71.68     
 TorchNL             | GPU   | 790.87     | 9.78      

2. Multi-Cutoff Pass [6.0, 14.0] Å

Benchmark for generating two neighbor lists simultaneously (e.g., GNN + Dispersion). (System: Ethanol 10k atoms)

Library	Strategy	Time (ms)
neighborlist-rs	Single Pass (Native)	313
Vesin	Filtered (14.0 -> 6.0)	414
Freud	Filtered (14.0 -> 6.0)	491
Matscipy	Filtered (14.0 -> 6.0)	667
ASE	Filtered (14.0 -> 6.0)	11680

3. Batch Throughput (GNN Workloads)

Throughput in systems per second for batches of isolated 100-atom molecules.

Batch Size	neighborlist-rs	TorchCluster	TorchNL
1	34,477	11,756	525
32	38,198	11,047	481
128	35,293	12,173	445

Note: TorchCluster does not support Periodic Boundary Conditions (PBC).

Key Optimizations

• Spatial Sorting: Uses Z-order (Morton) indexing to improve cache locality.
• Dynamic Configuration: Runtime-tunable thresholds for switching between SIMD Brute-Force and Cell Lists.
• Two-Pass Search: Minimizes heap allocations by pre-calculating neighbor counts.
• Cross-Platform SIMD: Optimized for both x86_64 (AVX2/FMA) and aarch64 (NEON).

Verification

Run Rust tests:

cargo test

Run Python tests:

pytest tests/

License

Licensed under either of:

• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT) at your option.