W-2021-08-26-all-icsd

../../_images/pareto263.png

The current structure dataset comprises 10959 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-volume157.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.

W-2021-08-26-all-icsd shows large prediction errors. They should be carefully used. Such an MLP is often accurate for reasonable structures, but it is not accurate for unrealistic structures.

Pareto optimals

Name

Time [ms] (1core/36cores)

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

Predictions

Files

pair-1

0.012 / 0.003

337.52 / 1.0886

–

–

pair-14

0.017 / 0.038

92.472 / 0.4574

–

–

pair-27

0.018 / 0.005

71.232 / 0.3994

–

–

pair-28

0.027 / 0.007

70.290 / 0.4028

–

–

pair-18

0.033 / 0.017

59.820 / 0.3959

–

–

pair-31

0.050 / 0.007

45.855 / 0.3472

–

–

pair-37

0.136 / 0.018

40.480 / 0.3254

–

–

gtinv-300

0.228 / 0.023

39.077 / 0.2501

–

–

gtinv-235

0.281 / 0.023

33.582 / 0.2314

–

–

gtinv-175

0.376 / 0.031

31.965 / 0.2543

–

–

gtinv-240

0.393 / 0.030

31.831 / 0.2541

–

–

gtinv-312

0.472 / 0.036

21.335 / 0.2182

–

–

gtinv-190

0.523 / 0.038

20.230 / 0.2055

–

–

gtinv-255

0.573 / 0.040

20.028 / 0.2042

–

–

gtinv-301

0.743 / 0.062

14.481 / 0.1791

–

–

gtinv-236

0.804 / 0.059

13.260 / 0.1711

–

–

gtinv-176

1.108 / 0.075

12.694 / 0.1753

–

–

gtinv-241

1.126 / 0.076

12.562 / 0.1745

–

–

gtinv-172

1.152 / 0.075

10.776 / 0.1561

–

–

gtinv-237

1.177 / 0.073

10.699 / 0.1559

–

–

gtinv-177

1.475 / 0.092

9.9404 / 0.1608

predictions

mlp.lammps input log

gtinv-242

1.506 / 0.092

9.9005 / 0.1605

predictions

mlp.lammps input log

gtinv-302

1.579 / 0.111

9.6455 / 0.1575

predictions

mlp.lammps input log

gtinv-191

1.600 / 0.102

9.5208 / 0.1539

predictions

mlp.lammps input log

gtinv-238

1.648 / 0.122

9.0494 / 0.1519

predictions

mlp.lammps input log

gtinv-174

1.973 / 0.134

8.1673 / 0.1428

predictions

mlp.lammps input log

gtinv-239

2.057 / 0.133

8.1358 / 0.1426

predictions

mlp.lammps input log

gtinv-192

2.106 / 0.124

7.9147 / 0.1426

predictions

mlp.lammps input log

gtinv-179

2.634 / 0.168

7.3562 / 0.1459

predictions

mlp.lammps input log

gtinv-244

2.680 / 0.171

7.3270 / 0.1457

predictions

mlp.lammps input log

gtinv-193

3.465 / 0.217

6.6052 / 0.1379

predictions

mlp.lammps input log

gtinv-258

3.556 / 0.220

6.5608 / 0.1376

predictions

mlp.lammps input log

gtinv-194

3.908 / 0.240

6.2620 / 0.1322

predictions

mlp.lammps input log

gtinv-259

4.078 / 0.244

6.2356 / 0.1321

predictions

mlp.lammps input log

gtinv-264

5.489 / 0.315

6.2278 / 0.1341

predictions

mlp.lammps input log

gtinv-269

6.917 / 0.387

6.0176 / 0.1365

predictions

mlp.lammps input log

gtinv-204

6.935 / 0.375

6.0097 / 0.1366

predictions

mlp.lammps input log

gtinv-209

8.915 / 0.479

5.8692 / 0.1405

predictions

mlp.lammps input log

gtinv-274

9.702 / 0.493

5.8394 / 0.1409

predictions

mlp.lammps input log

gtinv-332

13.113 / 0.653

5.5569 / 0.1384

predictions

mlp.lammps input log

gtinv-223

13.159 / 0.653

5.3779 / 0.1346

predictions

mlp.lammps input log

gtinv-288

13.824 / 0.659

5.3430 / 0.1343

predictions

mlp.lammps input log

gtinv-224

14.066 / 0.687

4.7876 / 0.1273

predictions

mlp.lammps input log

gtinv-289

14.825 / 0.712

4.7854 / 0.1271

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.