new_benchmark

[dhw059]

Matbench Pull Request Template

Thanks for making a PR to Matbench! We appreciate your contribution (like, a lot). To make things run smoothly, check out the following templates,
depending on what kind of PR you are making.

If you are making a benchmark submission (i.e., you have tried an algorithm on Matbench and want to appear on the leaderboard),
please use the template under Benchmark submissions.

If you are making changes to the core matbench code, data, or docs, please use the template under Core code/data/docs changes.

Benchmark submissions

Benchmark submissions can include a full benchmark on any of the benchmarks Matbench submits, as well as any subset of tasks within a benchmark (e.g., 3 Matbench v0.1 tasks your algorithm supports).

Brief description of your algorithm

You should a brief description of your algorithm in the pull request body. This can include any details you'd like.

Included files

If you are making a benchmark submission, please only include the submission as a folder in the /benchmarks directory with the format <benchmark_name>_<algorithm_name>. Your PR should have no other changes to the core code.
The submission should have these three required files, as indicated in the
docs:

Example

-- benchmarks
---- matbench_v0.1_my_algorithm
------ results.json.gz             # required filename
------ notebook.ipynb              # required filename
------ info.json                   # required filename

Please make sure each of these files has the information specified in the docs.

If you have other short/small files required for the notebook, please give a brief overview of what each one is used for and how to use it.

Label the pull request

Label the pull request with the new_benchmark label.

Core code/data/docs changes

Brief description of changes

Please include a brief description of the changes you are making, in bullet point format.

Tests

Indicate if your code requires new tests and whether they are included with your PR. ALL core code/data/docs changes adding new features must have new tests for them.

Closed issues or PRs

Indicate if your PR closes any currently open issues or supersedes any other currently open PRs.

Label the pull request

Label the pull request with the code or docs labels, depending on which one (or both) applies.

[dhw059]

Please check, the remaining tests will be conducted when the computing power is idle.

[dhw059]

This article introduces the composition-only model LGDCNN, similar to Finder and Crabnet on matbench, but outperforms them on almost all benchmark test sets, with better interpretability, fewer training parameters, and a composable model design architecture.

[dhw059]

We PR'ed two models, DenseGNN and LGDCNN-composition-only, which are currently under review at npj computational materials and Journal of Chemical Theory and Computation, respectively.