DOI: 10.5281/zenodo.18241955
title: Simulated datasets for LUFNet-CPC2026

description: Links to code and LUFnet paper:

1. [LUFnet Code]( https://github.com/Gobliu/LUFNet-CPC2026.git )

2. [Scalable neural network driven molecular dynamics simulation](https://doi.org/10.1016/j.cpc.2026.110036)

 

The .zip files contain the full dataset used in our LUFnet paper.




Category
Count (zip files)
Total Samples


Training set
13 files
180,000


validation set
1 file
20,000


Inference set : Input for LUFnet
3 files
3,000


Rollout steps for Velocity-Verlet (to compare with LUFnet)
3 files
3,000




Table 1. Dataset summary

 

The dataset is provided in .pt format, compressed as .zip files, and includes the training and validation datasets for LUFnet, as well as the inference dataset used as input for LUFnet.

The dataset were prepared following the procedure described in the Methods section of the LUFnet paper, using Monte Carlo–generated initial configurations and Velocity-Verlet algorithm.

 

Training and validation datasets

Due to the large storage requirements, the total training set is split into multiple .pt files and provided in compressed zip format. The training data consist of 13 subsets, which can be merged into a single .pt file to load the full training set. The validation set is stored as a single .pt file.





Training set (13 files) : Train_set_part*.zip




Validation set (1 file) : Valid_set.zip



 




filename (.pt format)
purpose
# Samples
# Particles
Phase
Ensemble
Data shape


Train_intput_NVE_part0_dpt15000.pt
training
15000
64
liquid
NVE
[15000, 3, 181, 64,3]


Train_intput_NVE_part1_dpt15000.pt
training
15000
64
liquid
NVE
[15000, 3, 181, 64,3]


Train_intput_NVE_part2_dpt15000.pt
training
15000
64
liquid
NVE
[15000, 3, 181, 64,3]


Train_intput_NVE_part3_dpt13500.pt
training
13500
64
liquid
NVE
[13500, 3, 181, 64,3]


Train_intput_NVE_part4_dpt13500.pt
training
13500
64
liquid
NVE
[13500, 3, 181, 64,3]


Train_intput_NVE_part5_dpt13500.pt
training
13500
64
liquid
NVE
[13500, 3, 181, 64,3]


Train_intput_NVE_part6_dpt13500.pt
training
13500
64
liquid
NVE
[13500, 3, 181, 64,3]


Train_intput_NVE_part7_dpt13500.pt
training
13500
64
liquid
NVE
[13500, 3, 181, 64,3]


Train_intput_NVE_part8_dpt13500.pt
training
13500
64
liquid
NVE
[13500, 3, 181, 64,3]


Train_intput_NVE_part9_dpt13500.pt
training
13500
64
liquid
NVE
[13500, 3, 181, 64,3]


Train_intput_NVE_part10_dpt13500.pt
training
13500
64
liquid
NVE
[13500, 3, 181, 64,3]


Train_intput_NVE_part11_dpt13500.pt
training
13500
64
liquid
NVE
[13500, 3, 181, 64,3]


Train_intput_NVE_part12_dpt13500.pt
training
13500
64
liquid
NVE
[13500, 3, 181, 64,3]


Valid_intput_NVE_dpt20000.pt
validation
20000
64
liquid
NVE
[20000, 3, 181, 64,3]




Table 2. Detailed information for Training and validation sets

 

Table 2 shows detailed information about the .pt files for training and validation sets. Within these files, the trajectories include the initial phase-space configuration by MC simulations and configurations saved every 100 integration steps over a total of 18,000 steps using the Velocity-Verlet. The effective time step for LUFnet can be adjusted to τ=0.1.

After merging the separate training subsets into a single .pt file, the loaded tensor for the training set has the shape [nsamples, 3, time points, nparticles, dim] = [180,000, 3, 181, 64, 3]. While the validation dataset has the shape [20,000, 3, 181, 64, 3].

In this study, phase-space points were read off at larger time intervals (τ=0.05 for the 3D LJ system) and used as the training labels.

 

Inference dataset

The inference data are prepared as the sequence of input for LUFnet, similar to training data generated from the initial configurations. The inference datasets cover different conditions, including various numbers of particles, n = 64, 128, 256. 





Input for LUFnet (3 folders inside zip) : Inference_input_LUFnet.zip



 




filename (.pt format)
purpose
# Samples
# Particles
Phase
Ensemble
Data shape




Inference_input_NVE_LUFnet/

n64rho0.85T0.9/n64rho0.85T0.9.pt

Input for LUFnet
1000
64
liquid
NVE
[1000, 3, 81, 64, 3]




Inference_input_NVE_LUFnet/

n128rho0.85T0.9/n128rho0.85T0.9.pt

Input for LUFnet
1000
128
liquid
NVE
[1000, 3, 81, 128, 3]




Inference_input_NVE_LUFnet/

n256rho0.85T0.9/n256rho0.85T0.9.pt

Input for LUFnet
1000
256
liquid
NVE
[1000, 3, 81, 256, 3]




Table 3. Detailed information for inference set 

 

Table 3 shows detailed information about the .pt files for inference set. Within these files, the trajectories include the initial phase-space configuration by MC simulations and configurations saved every 100 integration steps over a total of 8,000 steps using the Velocity-Verlet. The effective time step for LUFnet can be adjusted to τ=0.1.

The loaded tensor for input for LUFnet has shape [1000, 3, 81, nparticles, 3] for each condition. 

 

Rollout data for the Velocity-Verlet algorithm for comparison with LUFnet

Simulations were performed using both LUFnet and Velocity-Verlet algorithm up to t= 100. For comparison with LUFnet (time step of = 0.05), the Velocity Verlet starts at t = 0.35. The configuration at t=0.35 is taken from  the LUFnet input sequence and defined as Index 0. A time step of τ = 0.001 was used with a total of 100,000 rollout steps; these data are provided here.  





Rollout steps for Velocity-Verlet (3 folders inside zip) : Inference_Rollout_steps_Velocity_verlet.zip



 




filename (.pt format)
purpose
# Samples
# Particles
Phase
Ensemble
Data shape




Rollout_steps_NVT_Velocity_verlet/

n64rho0.85T0.9/

n64rho0.85T0.9gamma20.pt

Rollout steps for Velocity-Verlet (to compare with LUFnet)
1000
64
liquid
NVT
[1000, 3, 1001, 64, 3]




Rollout_steps_NVT_Velocity_verlet/

n128rho0.85T0.9/

n128rho0.85T0.9gamma20.pt

Rollout steps for Velocity-Verlet (to compare with LUFnet)
1000
128
liquid
NVT
[1000, 3, 1001, 128, 3]




Rollout_steps_NVT_Velocity_verlet/

n256rho0.85T0.9/

n256rho0.85T0.9gamma20.pt

Rollout steps for Velocity-Verlet (to compare with LUFnet)
1000
256
liquid
NVT
[1000, 3, 1001, 256, 3]




Table 4. Detailed information for rollout steps for Velocity-Verlet (to compare with LUFnet)

 

Table 4 shows detailed information about the .pt files for rollout steps for Velocity-Verlet. Within these files, 

the loaded tensor for Velocity Verlet rollout data has shape [1000, 3, 1001, nparticles, 3] for each condition. 

For LUFnet, the time integration step was set to τ = 0.05, corresponding to 2,000 rollout steps in the Implementation provided on our github repository.

The 1,000 inference samples are split into 5 groups of 200 samples each for RDF and energy calculations. Metrics are computed for each group, and the mean and standard deviation across the 5 groups are used for plotting and table, as shown in Figures 3 and 4, and Table 2 of the LUFnet paper.