Mo-2021-08-10-all-icsd

../../_images/pareto241.png

The current structure dataset comprises 10957 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-volume135.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.

Mo-2021-08-10-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-30

0.053 / 0.011

61.874 / 0.3730

–

–

gtinv-300

0.228 / 0.023

32.524 / 0.2459

–

–

gtinv-235

0.281 / 0.023

27.547 / 0.2292

–

–

gtinv-175

0.376 / 0.031

27.227 / 0.2452

–

–

gtinv-240

0.393 / 0.030

27.134 / 0.2449

–

–

gtinv-312

0.472 / 0.036

19.617 / 0.2219

–

–

gtinv-190

0.523 / 0.038

16.967 / 0.2053

–

–

gtinv-301

0.743 / 0.062

13.919 / 0.1867

–

–

gtinv-236

0.804 / 0.059

12.537 / 0.1792

–

–

gtinv-172

1.152 / 0.075

9.2757 / 0.1661

predictions

mlp.lammps input log

gtinv-237

1.177 / 0.073

9.2337 / 0.1658

predictions

mlp.lammps input log

gtinv-302

1.579 / 0.111

8.8299 / 0.1692

predictions

mlp.lammps input log

gtinv-238

1.648 / 0.122

8.5229 / 0.1668

predictions

mlp.lammps input log

gtinv-174

1.973 / 0.134

6.7777 / 0.1537

predictions

mlp.lammps input log

gtinv-239

2.057 / 0.133

6.7338 / 0.1535

predictions

mlp.lammps input log

gtinv-258

3.556 / 0.220

6.7221 / 0.1536

predictions

mlp.lammps input log

gtinv-194

3.908 / 0.240

5.7769 / 0.1451

predictions

mlp.lammps input log

gtinv-264

5.489 / 0.315

5.5805 / 0.1463

predictions

mlp.lammps input log

gtinv-332

13.113 / 0.653

5.4378 / 0.1513

predictions

mlp.lammps input log

gtinv-288

13.824 / 0.659

5.4119 / 0.1493

predictions

mlp.lammps input log

gtinv-224

14.066 / 0.687

4.8458 / 0.1417

predictions

mlp.lammps input log

gtinv-289

14.825 / 0.712

4.8222 / 0.1416

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.