Pd-2021-08-04-all-icsd

../../_images/pareto247.png

The current structure dataset comprises 12614 structures generated from unique ICSD prototype structures composed of single elements with zero oxidation state. A more detailed procedure is found in Phys. Rev. B 99, 214108 (2019). The procedure to estimate interatomic potentials from the dataset is found in Phys. Rev. B 99, 214108 (2019) and Phys. Rev. B 102, 174104 (2020).

Improvement from **-dataset-10000-all-icsd

  • More robust for structures with a small interatomic distance

  • More robust for structures with a large interatomic distance

  • More complex potential models are included.

  • MLPs are estimated without using DFT stress tensors.

  • MLPs are estimated by using small regression weights for energetically unstable structures.

Predictions using Pareto optimal MLPs

../../_images/prediction-ecoh-volume141.png

The cohesive energy and volume are obtained by performing a local structure optimization from the DFT equilibrium structure. In addition, the DFT equilibrium structure is obtained by optimizing a prototype structure included in ICSD, and the prototype is used as the structure legend in the figure. Therefore, the structure type of the converged structure is sometimes different from that shown in the legend even if the potential energy surface predicted by MLP is almost the same as the true one.

The other properties predicted by each Pareto optimal MLP are available from column Predictions in the following table.

Pareto optimals

Name

Time [ms] (1core/36cores)

RMSE [meV/atom]/[eV/A]

Predictions

Files

pair-14

0.017 / 0.038

22.248 / 0.1331

pair-27

0.018 / 0.005

12.953 / 0.0956

pair-28

0.027 / 0.007

12.716 / 0.0939

pair-29

0.039 / 0.009

12.619 / 0.0950

pair-31

0.050 / 0.007

11.152 / 0.0923

pair-32

0.051 / 0.010

10.668 / 0.0922

pair-33

0.069 / 0.012

10.443 / 0.0920

pair-34

0.096 / 0.016

10.392 / 0.0917

pair-37

0.136 / 0.018

8.8061 / 0.0887

predictions

mlp.lammps input log

pair-38

0.171 / 0.022

8.6594 / 0.0884

predictions

mlp.lammps input log

gtinv-300

0.228 / 0.023

5.1211 / 0.0690

predictions

mlp.lammps input log

gtinv-235

0.281 / 0.023

4.5868 / 0.0635

predictions

mlp.lammps input log

gtinv-170

0.282 / 0.022

4.4985 / 0.0637

predictions

mlp.lammps input log

gtinv-312

0.472 / 0.036

4.3464 / 0.0658

predictions

mlp.lammps input log

gtinv-190

0.523 / 0.038

4.1508 / 0.0611

predictions

mlp.lammps input log

gtinv-301

0.743 / 0.062

3.4842 / 0.0610

predictions

mlp.lammps input log

gtinv-236

0.804 / 0.059

3.1488 / 0.0572

predictions

mlp.lammps input log

gtinv-171

0.818 / 0.060

3.1167 / 0.0574

predictions

mlp.lammps input log

gtinv-304

1.069 / 0.073

3.1117 / 0.0609

predictions

mlp.lammps input log

gtinv-176

1.108 / 0.075

2.8514 / 0.0573

predictions

mlp.lammps input log

gtinv-172

1.152 / 0.075

2.4815 / 0.0533

predictions

mlp.lammps input log

gtinv-191

1.600 / 0.102

2.4797 / 0.0541

predictions

mlp.lammps input log

gtinv-256

1.780 / 0.109

2.4739 / 0.0539

predictions

mlp.lammps input log

gtinv-174

1.973 / 0.134

2.1597 / 0.0509

predictions

mlp.lammps input log

gtinv-192

2.106 / 0.124

2.1423 / 0.0503

predictions

mlp.lammps input log

gtinv-197

2.819 / 0.158

2.0931 / 0.0511

predictions

mlp.lammps input log

gtinv-262

2.839 / 0.166

2.0841 / 0.0508

predictions

mlp.lammps input log

gtinv-194

3.908 / 0.240

1.8503 / 0.0481

predictions

mlp.lammps input log

gtinv-264

5.489 / 0.315

1.8212 / 0.0489

predictions

mlp.lammps input log

gtinv-288

13.824 / 0.659

1.7961 / 0.0503

predictions

mlp.lammps input log

gtinv-224

14.066 / 0.687

1.6559 / 0.0473

predictions

mlp.lammps input log

gtinv-294

17.107 / 0.825

1.6312 / 0.0476

predictions

mlp.lammps input log

Column “Time” shows the time required to compute the energy and forces for 1 MD step and 1 atom, which is estimated from a simulation of 10 runs for a structure with 284 atoms using a workstation with Intel(R) Xeon(R) CPU E5-2695 v4 @ 2.10GHz. Note that the MLPs should be carefully used for extreme structures. The MLPs often return meaningless values for them.

  • All Pareto optimal MLPs are available here.