We lifted 5 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| atomicarchitects/dens | pwc_unofficial | 4 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| drop_path | Ran | atomicarchitects/dens/model/equiformer_v2/drop.py code served (permissive licence) · get_code("3ac6b7d76e8e3584") |
| gaussian | Ran | atomicarchitects/dens/model/equiformer_v2/gaussian_rbf.py code served (permissive licence) · get_code("294ba20b052bfbc7") |
| get_l_to_all_m_expand_index | Ran | atomicarchitects/dens/model/equiformer_v2/layer_norm.py code served (permissive licence) · get_code("d4c1ba3933fbb7ac") |
| init_edge_rot_mat | Ran | atomicarchitects/dens/model/equiformer_v2/edge_rot_mat.py code served (permissive licence) · get_code("fc635a57dd84d9f5") |
| get_normalization_layer | Not yet run | atomicarchitects/dens/model/equiformer_v2/layer_norm.py code served (permissive licence) · get_code("cb92c8232882c80e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The ability to discover new materials with desirable properties is critical for numerous applications from helping mitigate climate change to advances in next generation computing hardware. AI has the potential to accelerate materials discovery and design by more effectively exploring the chemical space compared to other computational methods or by trial-and-error. While substantial progress has been made on AI for materials data, benchmarks, and models, a barrier that has emerged is the lack of publicly available training data and open pre-trained models. To address this, we present a Meta FAIR release of the Open Materials 2024 (OMat24) large-scale open dataset and an accompanying set of pre-trained models. OMat24 contains over 110 million density functional theory (DFT) calculations focused on structural and compositional diversity. Our EquiformerV2 models achieve state-of-the-art performance on the Matbench Discovery leaderboard and are capable of predicting ground-state stability and formation energies to an F1 score above 0.9 and an accuracy of 20 meV/atom, respectively. We explore the impact of model size, auxiliary denoising objectives, and fine-tuning on performance across a range of datasets including OMat24, MPtraj, and Alexandria. The open release of the OMat24 dataset and models enables the research community to build upon our efforts and drive further advancements in AI-assisted materials science.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2410.12771")
get_code_for_paper("2410.12771")
have("2410.12771")
Connect an agent — have() is free.