We lifted 4 functions out of this paper's own repositories and ran 2 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 |
|---|---|---|
| virtualatoms/AnisoNet | canonical | 2 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| collate_fn | Ran | virtualatoms/AnisoNet/src/anisonet/data.py code served (permissive licence) · get_code("b297728280af4dee") |
| smooth_cutoff | Ran | virtualatoms/AnisoNet/src/anisonet/model.py code served (permissive licence) · get_code("62d02fdbf2eadc2e") |
| scatter_mean | Not yet run | virtualatoms/AnisoNet/src/anisonet/model.py code served (permissive licence) · get_code("649c35cc1d7d3563") |
| scatter_sum | Not yet run | virtualatoms/AnisoNet/src/anisonet/model.py code served (permissive licence) · get_code("710c8f643dd92ce8") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Anisotropy in crystals plays a pivotal role in many technological applications. For example, anisotropic electronic and thermal transport are thought to be beneficial for thermoelectric applications, while anisotropic mechanical properties are of interest for emerging metamaterials, and anisotropic dielectric materials have been suggested as a novel platform for dark matter detection. Understanding and tailoring anisotropy in crystals is therefore essential for the design of next-generation functional materials. To date, however, most data-driven approaches have focused on the prediction of scalar crystal properties, such as the spherically averaged dielectric tensor or the bulk and shear elastic moduli. Here, we adopt the latest approaches in equivariant graph neural networks to develop a model that can predict the full dielectric tensor of crystals. Our model, trained on the Materials Project dataset of c.a. 6,700 dielectric tensors, achieves state-of-the-art accuracy in scalar dielectric prediction in addition to capturing the directional response. We showcase the performance of the model by discovering crystals with almost isotropic connectivity but highly anisotropic dielectric tensors, thereby broadening our knowledge of the structure-property relationships in dielectric crystals.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2405.07915")
get_code_for_paper("2405.07915")
have("2405.07915")
Connect an agent — have() is free.