Fine tuning generative adversarial networks with universal force fields: application to two-dimensional topological insulators
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915231885688832 |
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| author | Tyner, Alexander C. |
| author_facet | Tyner, Alexander C. |
| contents | Despite rapid growth in use cases for generative artificial intelligence, its ability to design purpose built crystalline materials remains in a nascent phase. At the moment inverse design is generally accomplished by either constraining the training data set or producing a vast number of samples from a generator network and constraining the output via post-processing. We show that a general adversarial network trained to produce crystal structures from a latent space can be fine tuned through the introduction of advanced graph neural networks as discriminators, including a universal force field, to intrinsically bias the network towards generation of target materials. This is exemplified utilizing two-dimensional topological insulators as a sample target space. While a number of two-dimensional topological insulators have been predicted, the size of the band-gap, a measure of topological protection, remains a concern in most candidate compounds. The resulting generative network is shown to yield novel topological insulators. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04940 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fine tuning generative adversarial networks with universal force fields: application to two-dimensional topological insulators Tyner, Alexander C. Materials Science Disordered Systems and Neural Networks Mesoscale and Nanoscale Physics Despite rapid growth in use cases for generative artificial intelligence, its ability to design purpose built crystalline materials remains in a nascent phase. At the moment inverse design is generally accomplished by either constraining the training data set or producing a vast number of samples from a generator network and constraining the output via post-processing. We show that a general adversarial network trained to produce crystal structures from a latent space can be fine tuned through the introduction of advanced graph neural networks as discriminators, including a universal force field, to intrinsically bias the network towards generation of target materials. This is exemplified utilizing two-dimensional topological insulators as a sample target space. While a number of two-dimensional topological insulators have been predicted, the size of the band-gap, a measure of topological protection, remains a concern in most candidate compounds. The resulting generative network is shown to yield novel topological insulators. |
| title | Fine tuning generative adversarial networks with universal force fields: application to two-dimensional topological insulators |
| topic | Materials Science Disordered Systems and Neural Networks Mesoscale and Nanoscale Physics |
| url | https://arxiv.org/abs/2504.04940 |