La-2021-08-04-all-icsd

../../_images/pareto238.png

The current structure dataset comprises 11876 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-volume132.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-15

0.026 / 0.039

22.858 / 0.1326

pair-29

0.039 / 0.009

17.823 / 0.1234

pair-31

0.050 / 0.007

16.404 / 0.1193

pair-32

0.051 / 0.010

15.257 / 0.1172

pair-35

0.121 / 0.021

15.122 / 0.1163

pair-37

0.136 / 0.018

12.291 / 0.1072

pair-38

0.171 / 0.022

11.509 / 0.1058

gtinv-300

0.228 / 0.023

7.8467 / 0.0798

predictions

mlp.lammps input log

gtinv-235

0.281 / 0.023

6.5747 / 0.0715

predictions

mlp.lammps input log

gtinv-303

0.339 / 0.029

6.5296 / 0.0804

predictions

mlp.lammps input log

gtinv-175

0.376 / 0.031

6.4762 / 0.0765

predictions

mlp.lammps input log

gtinv-190

0.523 / 0.038

5.1790 / 0.0649

predictions

mlp.lammps input log

gtinv-255

0.573 / 0.040

5.0346 / 0.0643

predictions

mlp.lammps input log

gtinv-260

0.787 / 0.053

4.9548 / 0.0667

predictions

mlp.lammps input log

gtinv-236

0.804 / 0.059

4.9102 / 0.0630

predictions

mlp.lammps input log

gtinv-265

0.983 / 0.070

4.4769 / 0.0667

predictions

mlp.lammps input log

gtinv-176

1.108 / 0.075

4.2833 / 0.0639

predictions

mlp.lammps input log

gtinv-177

1.475 / 0.092

3.7774 / 0.0592

predictions

mlp.lammps input log

gtinv-313

1.590 / 0.102

3.6651 / 0.0620

predictions

mlp.lammps input log

gtinv-191

1.600 / 0.102

3.4494 / 0.0576

predictions

mlp.lammps input log

gtinv-192

2.106 / 0.124

3.1630 / 0.0539

predictions

mlp.lammps input log

gtinv-197

2.819 / 0.158

2.9593 / 0.0544

predictions

mlp.lammps input log

gtinv-267

3.516 / 0.196

2.8633 / 0.0554

predictions

mlp.lammps input log

gtinv-263

4.878 / 0.289

2.7948 / 0.0542

predictions

mlp.lammps input log

gtinv-264

5.489 / 0.315

2.6741 / 0.0521

predictions

mlp.lammps input log

gtinv-199

5.828 / 0.316

2.6410 / 0.0522

predictions

mlp.lammps input log

gtinv-222

6.837 / 0.369

2.4093 / 0.0515

predictions

mlp.lammps input log

gtinv-287

6.946 / 0.371

2.3906 / 0.0514

predictions

mlp.lammps input log

gtinv-227

8.349 / 0.454

2.3696 / 0.0519

predictions

mlp.lammps input log

gtinv-292

8.473 / 0.461

2.3484 / 0.0519

predictions

mlp.lammps input log

gtinv-288

13.824 / 0.659

2.2986 / 0.0518

predictions

mlp.lammps input log

gtinv-224

14.066 / 0.687

2.1613 / 0.0495

predictions

mlp.lammps input log

gtinv-289

14.825 / 0.712

2.1440 / 0.0494

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.