Sr-2021-08-04-all-icsd

../../_images/pareto257.png

The current structure dataset comprises 14710 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-volume151.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

102.04 / 0.1407

pair-14

0.017 / 0.038

29.688 / 0.0885

pair-27

0.018 / 0.005

23.859 / 0.0720

pair-15

0.026 / 0.039

9.3358 / 0.0350

pair-28

0.027 / 0.007

6.7245 / 0.0292

pair-29

0.039 / 0.009

6.6826 / 0.0291

pair-31

0.050 / 0.007

6.3472 / 0.0288

pair-32

0.051 / 0.010

6.0244 / 0.0273

pair-33

0.069 / 0.012

5.8935 / 0.0257

pair-34

0.096 / 0.016

5.5624 / 0.0251

pair-35

0.121 / 0.021

4.5551 / 0.0244

predictions

mlp.lammps input log

pair-38

0.171 / 0.022

3.9661 / 0.0231

predictions

mlp.lammps input log

pair-39

0.229 / 0.030

3.5952 / 0.0227

predictions

mlp.lammps input log

gtinv-303

0.339 / 0.029

2.8238 / 0.0142

predictions

mlp.lammps input log

gtinv-175

0.376 / 0.031

2.4892 / 0.0136

predictions

mlp.lammps input log

gtinv-240

0.393 / 0.030

2.4623 / 0.0135

predictions

mlp.lammps input log

gtinv-306

0.507 / 0.041

1.8377 / 0.0125

predictions

mlp.lammps input log

gtinv-180

0.543 / 0.041

1.7073 / 0.0121

predictions

mlp.lammps input log

gtinv-245

0.562 / 0.042

1.6757 / 0.0120

predictions

mlp.lammps input log

gtinv-195

0.748 / 0.053

1.5060 / 0.0117

predictions

mlp.lammps input log

gtinv-318

0.906 / 0.068

0.9962 / 0.0088

predictions

mlp.lammps input log

gtinv-265

0.983 / 0.070

0.8721 / 0.0081

predictions

mlp.lammps input log

gtinv-330

1.834 / 0.118

0.8343 / 0.0081

predictions

mlp.lammps input log

gtinv-220

1.890 / 0.117

0.7552 / 0.0075

predictions

mlp.lammps input log

gtinv-285

2.031 / 0.123

0.7447 / 0.0074

predictions

mlp.lammps input log

gtinv-266

2.966 / 0.169

0.7264 / 0.0071

predictions

mlp.lammps input log

gtinv-295

3.164 / 0.186

0.7128 / 0.0074

predictions

mlp.lammps input log

gtinv-267

3.516 / 0.196

0.6649 / 0.0066

predictions

mlp.lammps input log

gtinv-221

5.913 / 0.339

0.6331 / 0.0069

predictions

mlp.lammps input log

gtinv-331

5.973 / 0.392

0.6197 / 0.0066

predictions

mlp.lammps input log

gtinv-222

6.837 / 0.369

0.5517 / 0.0063

predictions

mlp.lammps input log

gtinv-287

6.946 / 0.371

0.5484 / 0.0063

predictions

mlp.lammps input log

gtinv-332

13.113 / 0.653

0.5294 / 0.0061

predictions

mlp.lammps input log

gtinv-223

13.159 / 0.653

0.5085 / 0.0056

predictions

mlp.lammps input log

gtinv-288

13.824 / 0.659

0.4930 / 0.0056

predictions

mlp.lammps input log

gtinv-224

14.066 / 0.687

0.4715 / 0.0053

predictions

mlp.lammps input log

gtinv-289

14.825 / 0.712

0.4632 / 0.0052

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.