Ca-2021-08-04-all-icsd

../../_images/pareto226.png

The current structure dataset comprises 14633 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-volume120.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

90.267 / 0.0872

pair-14

0.017 / 0.038

18.117 / 0.0449

pair-27

0.018 / 0.005

15.915 / 0.0457

pair-15

0.026 / 0.039

10.026 / 0.0426

pair-28

0.027 / 0.007

9.6806 / 0.0384

pair-18

0.033 / 0.017

8.7899 / 0.0415

pair-33

0.069 / 0.012

8.2966 / 0.0341

pair-21

0.095 / 0.017

7.3680 / 0.0360

pair-34

0.096 / 0.016

6.8536 / 0.0320

pair-35

0.121 / 0.021

6.3391 / 0.0325

pair-37

0.136 / 0.018

5.9931 / 0.0307

pair-38

0.171 / 0.022

5.6156 / 0.0298

pair-39

0.229 / 0.030

5.4001 / 0.0294

gtinv-303

0.339 / 0.029

2.2204 / 0.0136

predictions

mlp.lammps input log

gtinv-175

0.376 / 0.031

1.9056 / 0.0123

predictions

mlp.lammps input log

gtinv-240

0.393 / 0.030

1.8004 / 0.0122

predictions

mlp.lammps input log

gtinv-312

0.472 / 0.036

1.5943 / 0.0116

predictions

mlp.lammps input log

gtinv-190

0.523 / 0.038

1.4060 / 0.0104

predictions

mlp.lammps input log

gtinv-255

0.573 / 0.040

1.3698 / 0.0104

predictions

mlp.lammps input log

gtinv-195

0.748 / 0.053

1.0280 / 0.0095

predictions

mlp.lammps input log

gtinv-260

0.787 / 0.053

0.9472 / 0.0094

predictions

mlp.lammps input log

gtinv-265

0.983 / 0.070

0.8365 / 0.0095

predictions

mlp.lammps input log

gtinv-330

1.834 / 0.118

0.8040 / 0.0089

predictions

mlp.lammps input log

gtinv-220

1.890 / 0.117

0.6654 / 0.0080

predictions

mlp.lammps input log

gtinv-285

2.031 / 0.123

0.6265 / 0.0079

predictions

mlp.lammps input log

gtinv-290

2.494 / 0.149

0.6038 / 0.0078

predictions

mlp.lammps input log

gtinv-267

3.516 / 0.196

0.5982 / 0.0073

predictions

mlp.lammps input log

gtinv-272

5.037 / 0.266

0.5934 / 0.0077

predictions

mlp.lammps input log

gtinv-264

5.489 / 0.315

0.5875 / 0.0068

predictions

mlp.lammps input log

gtinv-277

5.863 / 0.307

0.5652 / 0.0078

predictions

mlp.lammps input log

gtinv-221

5.913 / 0.339

0.5543 / 0.0070

predictions

mlp.lammps input log

gtinv-286

6.000 / 0.346

0.5227 / 0.0069

predictions

mlp.lammps input log

gtinv-222

6.837 / 0.369

0.4815 / 0.0065

predictions

mlp.lammps input log

gtinv-287

6.946 / 0.371

0.4581 / 0.0064

predictions

mlp.lammps input log

gtinv-227

8.349 / 0.454

0.4287 / 0.0060

predictions

mlp.lammps input log

gtinv-292

8.473 / 0.461

0.4243 / 0.0061

predictions

mlp.lammps input log

gtinv-288

13.824 / 0.659

0.4235 / 0.0062

predictions

mlp.lammps input log

gtinv-289

14.825 / 0.712

0.4099 / 0.0060

predictions

mlp.lammps input log

gtinv-294

17.107 / 0.825

0.3842 / 0.0057

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.