gpwolfe commited on
Commit
9aedbd7
·
verified ·
1 Parent(s): 648523e

Add PWMLFF_feature_comparison_NPJ2023 files

Browse files
README.md ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ configs:
3
+ - config_name: default
4
+ data_files: "co/*.parquet"
5
+ - config_name: info
6
+ data_files: "ds.parquet"
7
+ - config_name: configuration_sets
8
+ data_files: "cs/*.parquet"
9
+ - config_name: config_set_mapping
10
+ data_files: "cs_co_map/*.parquet"
11
+ license: cc-by-4.0
12
+ tags:
13
+ - molecular dynamics
14
+ - mlip
15
+ - interatomic potential
16
+ pretty_name: PWMLFF feature comparison NPJ2023
17
+ ---
18
+ ### <details><summary>Cite this dataset </summary>Han, T., Li, J., Liu, L., Li, F., and Wang, L. _PWMLFF feature comparison NPJ2023_. ColabFit, 2024. https://doi.org/10.60732/209e0c9c</details>
19
+ #### This dataset has been curated and formatted for the ColabFit Exchange
20
+ #### This dataset is also available on the ColabFit Exchange:
21
+ https://materials.colabfit.org/id/DS_cgjdk1e2txjy_0
22
+ #### Visit the ColabFit Exchange to search additional datasets by author, description, element content and more.
23
+ https://materials.colabfit.org
24
+ <br><hr>
25
+ # Dataset Name
26
+ PWMLFF feature comparison NPJ2023
27
+ ### Description
28
+ Partial dataset for "Accuracy evaluation of different machine learning force field features". The included data is limited to that hosted directly on the repository at the related GitHub link. From publication abstract: Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development.
29
+ ### Dataset authors
30
+ Ting Han, Jie Li, Liping Liu, Fengyu Li, Lin-Wang Wang
31
+ ### Publication
32
+ https://www.doi.org/10.1088/1367-2630/acf2bb
33
+ ### Original data link
34
+ https://github.com/LonxunQuantum/PWMLFF_library/tree/main
35
+ ### License
36
+ CC-BY-4.0
37
+ ### Number of unique molecular configurations
38
+ 17255
39
+ ### Number of atoms
40
+ 918240
41
+ ### Elements included
42
+ C, H, Mg, Ni, O, Si
43
+ ### Properties included
44
+ energy, atomic forces, cauchy stress
45
+ <br>
46
+ <hr>
47
+
48
+ # Usage
49
+ - `ds.parquet` : Aggregated dataset information.
50
+ - `co/` directory: Configuration rows each include a structure, calculated properties, and metadata.
51
+ - `cs/` directory : Configuration sets are subsets of configurations grouped by some common characteristic. If `cs/` does not exist, no configurations sets have been defined for this dataset.
52
+ - `cs_co_map/` directory : The mapping of configurations to configuration sets (if defined).
53
+ <br>
54
+ #### ColabFit Exchange documentation includes descriptions of content and example code for parsing parquet files:
55
+ - [Parquet parsing: example code](https://materials.colabfit.org/docs/how_to_use_parquet)
56
+ - [Dataset info schema](https://materials.colabfit.org/docs/dataset_schema)
57
+ - [Configuration schema](https://materials.colabfit.org/docs/configuration_schema)
58
+ - [Configuration set schema](https://materials.colabfit.org/docs/configuration_set_schema)
59
+ - [Configuration set to configuration mapping schema](https://materials.colabfit.org/docs/cs_co_mapping_schema)
co/co_0.parquet ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c85f02f9e94e2860d5cddd6c6c289c1b7dd5f442813f48e893f44889b86fae06
3
+ size 50115590
cs/cs_0.parquet ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5b8aaf9c59e5749da9ed76f866bcf453155049c1018e2c1e9832ef2db6782194
3
+ size 7924
cs_co_map/cs_co_map_0.parquet ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:bec9b449362ac1f19774215d76f884197c455df4252ece6902ad6d0a01230fdd
3
+ size 222958
ds.parquet ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:763dfef9ad3d363244f0286d660e4b8bdac299b09b9ee3c6bd537546b441efc8
3
+ size 23451