Sn-2021-08-04-all-icsd

../../_images/pareto256.png

The current structure dataset comprises 13973 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-volume150.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-14

0.017 / 0.038

65.720 / 0.1701

pair-27

0.018 / 0.005

30.552 / 0.1026

pair-28

0.027 / 0.007

23.799 / 0.0997

pair-31

0.050 / 0.007

20.944 / 0.0942

pair-32

0.051 / 0.010

20.365 / 0.0923

pair-33

0.069 / 0.012

19.358 / 0.0916

pair-34

0.096 / 0.016

19.212 / 0.0941

pair-35

0.121 / 0.021

18.830 / 0.0938

pair-37

0.136 / 0.018

16.108 / 0.0866

pair-38

0.171 / 0.022

14.537 / 0.0852

gtinv-300

0.228 / 0.023

7.5747 / 0.0541

predictions

mlp.lammps input log

gtinv-235

0.281 / 0.023

5.5451 / 0.0473

predictions

mlp.lammps input log

gtinv-240

0.393 / 0.030

5.5221 / 0.0476

predictions

mlp.lammps input log

gtinv-312

0.472 / 0.036

4.7291 / 0.0471

predictions

mlp.lammps input log

gtinv-190

0.523 / 0.038

4.2091 / 0.0433

predictions

mlp.lammps input log

gtinv-255

0.573 / 0.040

4.0489 / 0.0432

predictions

mlp.lammps input log

gtinv-260

0.787 / 0.053

4.0136 / 0.0421

predictions

mlp.lammps input log

gtinv-265

0.983 / 0.070

3.8314 / 0.0424

predictions

mlp.lammps input log

gtinv-177

1.475 / 0.092

3.5981 / 0.0423

predictions

mlp.lammps input log

gtinv-242

1.506 / 0.092

3.5088 / 0.0420

predictions

mlp.lammps input log

gtinv-191

1.600 / 0.102

3.2244 / 0.0404

predictions

mlp.lammps input log

gtinv-342

1.775 / 0.083

3.1849 / 0.0393

predictions

mlp.lammps input log

gtinv-256

1.780 / 0.109

3.0934 / 0.0396

predictions

mlp.lammps input log

gtinv-345

1.915 / 0.092

2.8088 / 0.0390

predictions

mlp.lammps input log

gtinv-261

2.341 / 0.142

2.7727 / 0.0388

predictions

mlp.lammps input log

gtinv-197

2.819 / 0.158

2.7088 / 0.0387

predictions

mlp.lammps input log

gtinv-262

2.839 / 0.166

2.6436 / 0.0380

predictions

mlp.lammps input log

gtinv-343

2.986 / 0.131

2.5616 / 0.0365

predictions

mlp.lammps input log

gtinv-346

3.371 / 0.155

2.5021 / 0.0377

predictions

mlp.lammps input log

gtinv-354

3.661 / 0.154

2.3894 / 0.0361

predictions

mlp.lammps input log

gtinv-347

5.540 / 0.290

2.2379 / 0.0365

predictions

mlp.lammps input log

gtinv-232

10.649 / 0.547

2.2362 / 0.0357

predictions

mlp.lammps input log

gtinv-223

13.159 / 0.653

2.1946 / 0.0342

predictions

mlp.lammps input log

gtinv-288

13.824 / 0.659

2.1657 / 0.0336

predictions

mlp.lammps input log

gtinv-224

14.066 / 0.687

2.1154 / 0.0337

predictions

mlp.lammps input log

gtinv-289

14.825 / 0.712

2.1028 / 0.0331

predictions

mlp.lammps input log

gtinv-294

17.107 / 0.825

2.0788 / 0.0330

predictions

mlp.lammps input log

gtinv-298

21.560 / 1.090

2.0708 / 0.0340

predictions

mlp.lammps input log

gtinv-234

21.888 / 1.017

1.9995 / 0.0340

predictions

mlp.lammps input log

gtinv-299

22.419 / 1.031

1.9896 / 0.0335

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.