Sc-2021-08-04-all-icsd

../../_images/pareto254.png

The current structure dataset comprises 11081 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-volume148.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.

Sc-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-1

0.012 / 0.003

153.49 / 0.2744

pair-14

0.017 / 0.038

34.397 / 0.1756

pair-27

0.018 / 0.005

32.680 / 0.1602

pair-18

0.033 / 0.017

27.326 / 0.1692

pair-31

0.050 / 0.007

26.963 / 0.1546

pair-33

0.069 / 0.012

26.785 / 0.1553

pair-24

0.132 / 0.019

24.595 / 0.1606

pair-37

0.136 / 0.018

21.702 / 0.1445

pair-38

0.171 / 0.022

21.020 / 0.1427

gtinv-300

0.228 / 0.023

8.8065 / 0.0954

predictions

mlp.lammps input log

gtinv-235

0.281 / 0.023

7.5349 / 0.0856

predictions

mlp.lammps input log

gtinv-312

0.472 / 0.036

6.6784 / 0.0854

predictions

mlp.lammps input log

gtinv-190

0.523 / 0.038

5.6326 / 0.0764

predictions

mlp.lammps input log

gtinv-255

0.573 / 0.040

5.6184 / 0.0765

predictions

mlp.lammps input log

gtinv-301

0.743 / 0.062

5.4756 / 0.0836

predictions

mlp.lammps input log

gtinv-236

0.804 / 0.059

5.0764 / 0.0755

predictions

mlp.lammps input log

gtinv-176

1.108 / 0.075

5.0350 / 0.0788

predictions

mlp.lammps input log

gtinv-241

1.126 / 0.076

5.0099 / 0.0776

predictions

mlp.lammps input log

gtinv-172

1.152 / 0.075

3.8238 / 0.0685

predictions

mlp.lammps input log

gtinv-177

1.475 / 0.092

3.6069 / 0.0684

predictions

mlp.lammps input log

gtinv-242

1.506 / 0.092

3.4978 / 0.0697

predictions

mlp.lammps input log

gtinv-174

1.973 / 0.134

3.3464 / 0.0653

predictions

mlp.lammps input log

gtinv-192

2.106 / 0.124

2.7826 / 0.0632

predictions

mlp.lammps input log

gtinv-257

2.164 / 0.129

2.7698 / 0.0634

predictions

mlp.lammps input log

gtinv-194

3.908 / 0.240

2.6348 / 0.0605

predictions

mlp.lammps input log

gtinv-259

4.078 / 0.244

2.6183 / 0.0605

predictions

mlp.lammps input log

gtinv-264

5.489 / 0.315

2.4839 / 0.0607

predictions

mlp.lammps input log

gtinv-222

6.837 / 0.369

2.3814 / 0.0590

predictions

mlp.lammps input log

gtinv-287

6.946 / 0.371

2.3621 / 0.0588

predictions

mlp.lammps input log

gtinv-224

14.066 / 0.687

2.3010 / 0.0582

predictions

mlp.lammps input log

gtinv-289

14.825 / 0.712

2.2589 / 0.0579

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.