Y-2021-08-04-all-icsd

../../_images/pareto264.png

The current structure dataset comprises 11108 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-volume158.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-1

0.012 / 0.003

152.27 / 0.2149

pair-14

0.017 / 0.038

34.118 / 0.1448

pair-27

0.018 / 0.005

30.309 / 0.1317

pair-15

0.026 / 0.039

29.929 / 0.1387

pair-18

0.033 / 0.017

27.645 / 0.1345

pair-31

0.050 / 0.007

27.190 / 0.1240

pair-32

0.051 / 0.010

25.393 / 0.1202

pair-24

0.132 / 0.019

23.800 / 0.1266

pair-37

0.136 / 0.018

19.938 / 0.1122

pair-38

0.171 / 0.022

19.250 / 0.1119

gtinv-1

0.199 / 0.020

18.906 / 0.1171

gtinv-300

0.228 / 0.023

9.0850 / 0.0748

predictions

mlp.lammps input log

gtinv-235

0.281 / 0.023

9.0240 / 0.0675

predictions

mlp.lammps input log

gtinv-303

0.339 / 0.029

7.7931 / 0.0739

predictions

mlp.lammps input log

gtinv-175

0.376 / 0.031

7.3532 / 0.0675

predictions

mlp.lammps input log

gtinv-312

0.472 / 0.036

5.6772 / 0.0648

predictions

mlp.lammps input log

gtinv-190

0.523 / 0.038

5.3185 / 0.0585

predictions

mlp.lammps input log

gtinv-304

1.069 / 0.073

4.4721 / 0.0608

predictions

mlp.lammps input log

gtinv-176

1.108 / 0.075

4.3684 / 0.0558

predictions

mlp.lammps input log

gtinv-241

1.126 / 0.076

4.3594 / 0.0556

predictions

mlp.lammps input log

gtinv-172

1.152 / 0.075

3.3598 / 0.0503

predictions

mlp.lammps input log

gtinv-177

1.475 / 0.092

2.8914 / 0.0501

predictions

mlp.lammps input log

gtinv-174

1.973 / 0.134

2.8635 / 0.0488

predictions

mlp.lammps input log

gtinv-192

2.106 / 0.124

2.4303 / 0.0468

predictions

mlp.lammps input log

gtinv-257

2.164 / 0.129

2.4110 / 0.0465

predictions

mlp.lammps input log

gtinv-197

2.819 / 0.158

2.2136 / 0.0466

predictions

mlp.lammps input log

gtinv-262

2.839 / 0.166

2.2130 / 0.0465

predictions

mlp.lammps input log

gtinv-194

3.908 / 0.240

2.2006 / 0.0451

predictions

mlp.lammps input log

gtinv-264

5.489 / 0.315

1.9875 / 0.0450

predictions

mlp.lammps input log

gtinv-199

5.828 / 0.316

1.9864 / 0.0451

predictions

mlp.lammps input log

gtinv-222

6.837 / 0.369

1.9233 / 0.0443

predictions

mlp.lammps input log

gtinv-287

6.946 / 0.371

1.9169 / 0.0442

predictions

mlp.lammps input log

gtinv-232

10.649 / 0.547

1.8933 / 0.0450

predictions

mlp.lammps input log

gtinv-297

10.655 / 0.543

1.8926 / 0.0449

predictions

mlp.lammps input log

gtinv-224

14.066 / 0.687

1.7727 / 0.0429

predictions

mlp.lammps input log

gtinv-289

14.825 / 0.712

1.7691 / 0.0429

predictions

mlp.lammps input log

gtinv-294

17.107 / 0.825

1.7496 / 0.0429

predictions

mlp.lammps input log

gtinv-229

17.857 / 0.829

1.7490 / 0.0430

predictions

mlp.lammps input log

gtinv-234

21.888 / 1.017

1.7259 / 0.0436

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.