Ta-2021-08-04-all-icsd

../../_images/pareto258.png

The current structure dataset comprises 10990 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-volume152.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.

Ta-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

362.79 / 0.8737

pair-14

0.017 / 0.038

56.391 / 0.3226

pair-27

0.018 / 0.005

45.785 / 0.3163

pair-18

0.033 / 0.017

43.422 / 0.3145

pair-31

0.050 / 0.007

35.735 / 0.2993

pair-37

0.136 / 0.018

29.210 / 0.2852

gtinv-300

0.228 / 0.023

20.740 / 0.2017

gtinv-235

0.281 / 0.023

18.968 / 0.1918

gtinv-175

0.376 / 0.031

18.346 / 0.1950

gtinv-312

0.472 / 0.036

13.066 / 0.1809

gtinv-190

0.523 / 0.038

11.786 / 0.1721

gtinv-255

0.573 / 0.040

11.696 / 0.1713

gtinv-301

0.743 / 0.062

9.8067 / 0.1630

predictions

mlp.lammps input log

gtinv-236

0.804 / 0.059

9.0505 / 0.1564

predictions

mlp.lammps input log

gtinv-172

1.152 / 0.075

6.5610 / 0.1408

predictions

mlp.lammps input log

gtinv-237

1.177 / 0.073

6.4819 / 0.1400

predictions

mlp.lammps input log

gtinv-313

1.590 / 0.102

6.4778 / 0.1498

predictions

mlp.lammps input log

gtinv-191

1.600 / 0.102

6.0497 / 0.1445

predictions

mlp.lammps input log

gtinv-256

1.780 / 0.109

6.0159 / 0.1435

predictions

mlp.lammps input log

gtinv-174

1.973 / 0.134

5.4621 / 0.1332

predictions

mlp.lammps input log

gtinv-239

2.057 / 0.133

5.3843 / 0.1324

predictions

mlp.lammps input log

gtinv-192

2.106 / 0.124

5.1820 / 0.1311

predictions

mlp.lammps input log

gtinv-257

2.164 / 0.129

5.1164 / 0.1304

predictions

mlp.lammps input log

gtinv-193

3.465 / 0.217

4.8856 / 0.1361

predictions

mlp.lammps input log

gtinv-258

3.556 / 0.220

4.8082 / 0.1353

predictions

mlp.lammps input log

gtinv-194

3.908 / 0.240

4.3411 / 0.1254

predictions

mlp.lammps input log

gtinv-259

4.078 / 0.244

4.2568 / 0.1247

predictions

mlp.lammps input log

gtinv-287

6.946 / 0.371

4.2386 / 0.1265

predictions

mlp.lammps input log

gtinv-223

13.159 / 0.653

4.1359 / 0.1320

predictions

mlp.lammps input log

gtinv-224

14.066 / 0.687

3.7737 / 0.1219

predictions

mlp.lammps input log

gtinv-289

14.825 / 0.712

3.7397 / 0.1213

predictions

mlp.lammps input log

gtinv-294

17.107 / 0.825

3.6580 / 0.1220

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.