Skip to content

matbench_v0.1: CrabNet v1.2.1

Algorithm description:

Fit CrabNet with default hyperparameters to serve as a baseline for Ax+CrabNet v1.2.1.

Notes:

A Jupyter notebook is provided which contains additional details about the run of the algorithm.

Raw data download and example notebook available on the matbench repo.

References (in bibtex format):

['@article{Wang2021crabnet,  author = {Wang, Anthony Yu-Tung and Kauwe, Steven '
 'K. and Murdock, Ryan J. and Sparks, Taylor D.},  year = {2021},  title = '
 '{Compositionally restricted attention-based network for materials property '
 'predictions},  pages = {77},  volume = {7},  number = {1},  doi = '
 '{10.1038/s41524-021-00545-1},  publisher = {{Nature Publishing Group}},  '
 'shortjournal = {npj Comput. Mater.},  journal = {npj Computational '
 'Materials}',
 '@article{wang_kauwe_murdock_sparks_2021, place={Cambridge}, '
 'title={Compositionally-Restricted Attention-Based Network for Materials '
 'Property Prediction}, DOI={10.26434/chemrxiv.11869026.v3}, '
 'journal={ChemRxiv}, publisher={Cambridge Open Engage}, author={Wang, Anthony '
 'and Kauwe, Steven and Murdock, Ryan and Sparks, Taylor}, year={2021}} This '
 'content is a preprint and has not been peer-reviewed.']

User metadata:

{'algorithm_version': '1.2.1'}

Metadata:

tasks recorded 1/13
complete?
composition complete?
structure complete?
regression complete?
classification complete?

Software Requirements

{'python': [['crabnet==1.2.1', 'scikit_learn==1.0.2', 'matbench==0.5']]}

Task data:

matbench_expt_gap

Fold scores
fold mae rmse mape* max_error
fold_0 0.3489 0.8079 0.4441 5.6781
fold_1 0.3674 0.8399 0.3349 7.0404
fold_2 0.4106 1.0092 0.4539 10.2572
fold_3 0.3677 0.8437 0.4181 6.1608
fold_4 0.3839 0.9019 0.4944 7.4912
Fold score stats
metric mean max min std
mae 0.3757 0.4106 0.3489 0.0207
rmse 0.8805 1.0092 0.8079 0.0711
mape* 0.4291 0.4944 0.3349 0.0531
max_error 7.3256 10.2572 5.6781 1.5984
Fold parameters
fold params dict
fold_0 {'N': 3, 'adam': False, 'alpha': 0.5, 'base_lr': 0.0001, 'betas': [0.9, 0.999], 'bias': False, 'criterion': None, 'd_model': 512, 'dim_feedforward': 2048, 'dropout': 0.1, 'elem_prop': 'mat2vec', 'emb_...
fold_1 {'N': 3, 'adam': False, 'alpha': 0.5, 'base_lr': 0.0001, 'betas': [0.9, 0.999], 'bias': False, 'criterion': None, 'd_model': 512, 'dim_feedforward': 2048, 'dropout': 0.1, 'elem_prop': 'mat2vec', 'emb_...
fold_2 {'N': 3, 'adam': False, 'alpha': 0.5, 'base_lr': 0.0001, 'betas': [0.9, 0.999], 'bias': False, 'criterion': None, 'd_model': 512, 'dim_feedforward': 2048, 'dropout': 0.1, 'elem_prop': 'mat2vec', 'emb_...
fold_3 {'N': 3, 'adam': False, 'alpha': 0.5, 'base_lr': 0.0001, 'betas': [0.9, 0.999], 'bias': False, 'criterion': None, 'd_model': 512, 'dim_feedforward': 2048, 'dropout': 0.1, 'elem_prop': 'mat2vec', 'emb_...
fold_4 {'N': 3, 'adam': False, 'alpha': 0.5, 'base_lr': 0.0001, 'betas': [0.9, 0.999], 'bias': False, 'criterion': None, 'd_model': 512, 'dim_feedforward': 2048, 'dropout': 0.1, 'elem_prop': 'mat2vec', 'emb_...