Na-2021-08-04-all-icsd¶

The current structure dataset comprises 14758 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-volume136.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	30.971 / 0.0525	–	–
pair-27	0.018 / 0.005	12.271 / 0.0348	–	–
pair-15	0.026 / 0.039	8.9840 / 0.0282	predictions	`mlp.lammps` `input` `log`
pair-28	0.027 / 0.007	3.7172 / 0.0162	predictions	`mlp.lammps` `input` `log`
pair-31	0.050 / 0.007	3.5692 / 0.0159	predictions	`mlp.lammps` `input` `log`
gtinv-303	0.339 / 0.029	1.6329 / 0.0087	predictions	`mlp.lammps` `input` `log`
gtinv-175	0.376 / 0.031	1.5794 / 0.0085	predictions	`mlp.lammps` `input` `log`
gtinv-240	0.393 / 0.030	1.5676 / 0.0084	predictions	`mlp.lammps` `input` `log`
gtinv-312	0.472 / 0.036	1.4422 / 0.0065	predictions	`mlp.lammps` `input` `log`
gtinv-190	0.523 / 0.038	1.4243 / 0.0064	predictions	`mlp.lammps` `input` `log`
gtinv-255	0.573 / 0.040	1.4177 / 0.0064	predictions	`mlp.lammps` `input` `log`
gtinv-304	1.069 / 0.073	1.3848 / 0.0078	predictions	`mlp.lammps` `input` `log`
gtinv-176	1.108 / 0.075	1.3635 / 0.0077	predictions	`mlp.lammps` `input` `log`
gtinv-241	1.126 / 0.076	1.3520 / 0.0077	predictions	`mlp.lammps` `input` `log`
gtinv-177	1.475 / 0.092	1.3444 / 0.0076	predictions	`mlp.lammps` `input` `log`
gtinv-242	1.506 / 0.092	1.3337 / 0.0076	predictions	`mlp.lammps` `input` `log`
gtinv-313	1.590 / 0.102	1.3253 / 0.0065	predictions	`mlp.lammps` `input` `log`
gtinv-191	1.600 / 0.102	1.3169 / 0.0064	predictions	`mlp.lammps` `input` `log`
gtinv-256	1.780 / 0.109	1.3047 / 0.0064	predictions	`mlp.lammps` `input` `log`
gtinv-257	2.164 / 0.129	1.2976 / 0.0063	predictions	`mlp.lammps` `input` `log`
gtinv-261	2.341 / 0.142	1.1777 / 0.0054	predictions	`mlp.lammps` `input` `log`
gtinv-262	2.839 / 0.166	1.1674 / 0.0049	predictions	`mlp.lammps` `input` `log`
gtinv-263	4.878 / 0.289	1.1364 / 0.0052	predictions	`mlp.lammps` `input` `log`
gtinv-264	5.489 / 0.315	1.1166 / 0.0051	predictions	`mlp.lammps` `input` `log`
gtinv-221	5.913 / 0.339	1.0613 / 0.0048	predictions	`mlp.lammps` `input` `log`
gtinv-286	6.000 / 0.346	1.0566 / 0.0048	predictions	`mlp.lammps` `input` `log`
gtinv-232	10.649 / 0.547	1.0160 / 0.0043	predictions	`mlp.lammps` `input` `log`
gtinv-297	10.655 / 0.543	1.0131 / 0.0042	predictions	`mlp.lammps` `input` `log`
gtinv-332	13.113 / 0.653	0.9975 / 0.0050	predictions	`mlp.lammps` `input` `log`
gtinv-223	13.159 / 0.653	0.9825 / 0.0048	predictions	`mlp.lammps` `input` `log`
gtinv-288	13.824 / 0.659	0.9795 / 0.0048	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.