Hf-2021-08-04-all-icsd

../../_images/pareto233.png

The current structure dataset comprises 12914 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-volume127.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.

Hf-2021-08-04-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-14

0.017 / 0.038

38.276 / 0.2848

–

–

pair-27

0.018 / 0.005

36.570 / 0.2770

–

–

pair-18

0.033 / 0.017

35.764 / 0.2835

–

–

pair-31

0.050 / 0.007

32.370 / 0.2645

–

–

pair-32

0.051 / 0.010

28.778 / 0.2578

–

–

pair-37

0.136 / 0.018

23.755 / 0.2504

–

–

gtinv-300

0.228 / 0.023

13.977 / 0.1608

–

–

gtinv-235

0.281 / 0.023

12.296 / 0.1503

–

–

gtinv-240

0.393 / 0.030

11.130 / 0.1558

–

–

gtinv-312

0.472 / 0.036

8.8073 / 0.1445

predictions

mlp.lammps input log

gtinv-190

0.523 / 0.038

8.2231 / 0.1375

predictions

mlp.lammps input log

gtinv-255

0.573 / 0.040

8.1361 / 0.1370

predictions

mlp.lammps input log

gtinv-236

0.804 / 0.059

8.0672 / 0.1329

predictions

mlp.lammps input log

gtinv-304

1.069 / 0.073

7.6467 / 0.1399

predictions

mlp.lammps input log

gtinv-176

1.108 / 0.075

7.0178 / 0.1352

predictions

mlp.lammps input log

gtinv-241

1.126 / 0.076

7.0160 / 0.1341

predictions

mlp.lammps input log

gtinv-172

1.152 / 0.075

6.7426 / 0.1207

predictions

mlp.lammps input log

gtinv-237

1.177 / 0.073

6.6579 / 0.1202

predictions

mlp.lammps input log

gtinv-177

1.475 / 0.092

5.9288 / 0.1216

predictions

mlp.lammps input log

gtinv-242

1.506 / 0.092

5.8282 / 0.1209

predictions

mlp.lammps input log

gtinv-191

1.600 / 0.102

5.6228 / 0.1234

predictions

mlp.lammps input log

gtinv-256

1.780 / 0.109

5.5359 / 0.1228

predictions

mlp.lammps input log

gtinv-192

2.106 / 0.124

4.8876 / 0.1124

predictions

mlp.lammps input log

gtinv-257

2.164 / 0.129

4.8127 / 0.1118

predictions

mlp.lammps input log

gtinv-197

2.819 / 0.158

4.7945 / 0.1140

predictions

mlp.lammps input log

gtinv-193

3.465 / 0.217

4.6825 / 0.1149

predictions

mlp.lammps input log

gtinv-267

3.516 / 0.196

4.6634 / 0.1166

predictions

mlp.lammps input log

gtinv-258

3.556 / 0.220

4.6184 / 0.1144

predictions

mlp.lammps input log

gtinv-194

3.908 / 0.240

4.2975 / 0.1066

predictions

mlp.lammps input log

gtinv-259

4.078 / 0.244

4.2286 / 0.1060

predictions

mlp.lammps input log

gtinv-222

6.837 / 0.369

3.7926 / 0.1064

predictions

mlp.lammps input log

gtinv-224

14.066 / 0.687

3.5443 / 0.1013

predictions

mlp.lammps input log

gtinv-294

17.107 / 0.825

3.5154 / 0.1020

predictions

mlp.lammps input log

gtinv-229

17.857 / 0.829

3.5066 / 0.1025

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.