Spectral operator representations
Abstract Machine learning in atomistic materials science has grown to become a powerful tool, with most approaches focusing on atomic geometry, typically decomposed into local atomic environments. This approach, while well-suited for machine-learned interatomic potentials, is conceptually at odds wi...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-12-01
|
| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-024-01446-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846136944457678848 |
|---|---|
| author | Austin Zadoks Antimo Marrazzo Nicola Marzari |
| author_facet | Austin Zadoks Antimo Marrazzo Nicola Marzari |
| author_sort | Austin Zadoks |
| collection | DOAJ |
| description | Abstract Machine learning in atomistic materials science has grown to become a powerful tool, with most approaches focusing on atomic geometry, typically decomposed into local atomic environments. This approach, while well-suited for machine-learned interatomic potentials, is conceptually at odds with learning complex intrinsic properties of materials, often driven by spectral properties commonly represented in reciprocal space (e.g., band gaps or mobilities) which cannot be readily partitioned in real space. For such applications, methods that represent the electronic rather than the atomic structure could be more promising. In this work, we present a general framework focused on electronic-structure descriptors that take advantage of the natural symmetries and inherent interpretability of physical models. We apply this framework first to material similarity and then to accelerated screening, where a model trained on 217 materials correctly labels 75% of entries in the Materials Cloud 3D database, which meet common screening criteria for promising transparent-conducting materials. |
| format | Article |
| id | doaj-art-db01f22b7cea4d7bb0430892b6fd7b80 |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-db01f22b7cea4d7bb0430892b6fd7b802024-12-08T12:37:36ZengNature Portfolionpj Computational Materials2057-39602024-12-0110111210.1038/s41524-024-01446-9Spectral operator representationsAustin Zadoks0Antimo Marrazzo1Nicola Marzari2Theory and Simulation of Materials (THEOS), École Polytechnique Fédérale de LausanneDipartimento di Fisica, Università di TriesteTheory and Simulation of Materials (THEOS), École Polytechnique Fédérale de LausanneAbstract Machine learning in atomistic materials science has grown to become a powerful tool, with most approaches focusing on atomic geometry, typically decomposed into local atomic environments. This approach, while well-suited for machine-learned interatomic potentials, is conceptually at odds with learning complex intrinsic properties of materials, often driven by spectral properties commonly represented in reciprocal space (e.g., band gaps or mobilities) which cannot be readily partitioned in real space. For such applications, methods that represent the electronic rather than the atomic structure could be more promising. In this work, we present a general framework focused on electronic-structure descriptors that take advantage of the natural symmetries and inherent interpretability of physical models. We apply this framework first to material similarity and then to accelerated screening, where a model trained on 217 materials correctly labels 75% of entries in the Materials Cloud 3D database, which meet common screening criteria for promising transparent-conducting materials.https://doi.org/10.1038/s41524-024-01446-9 |
| spellingShingle | Austin Zadoks Antimo Marrazzo Nicola Marzari Spectral operator representations npj Computational Materials |
| title | Spectral operator representations |
| title_full | Spectral operator representations |
| title_fullStr | Spectral operator representations |
| title_full_unstemmed | Spectral operator representations |
| title_short | Spectral operator representations |
| title_sort | spectral operator representations |
| url | https://doi.org/10.1038/s41524-024-01446-9 |
| work_keys_str_mv | AT austinzadoks spectraloperatorrepresentations AT antimomarrazzo spectraloperatorrepresentations AT nicolamarzari spectraloperatorrepresentations |