Predicting Atomistic Transitions with Transformers
Accurate knowledge of the atomistic transition pathways in materials and material surfaces is crucial for many material science problems. However, conventional simulation techniques used to find these transitions are extremely computationally intensive. Even with large-scale, accelerated material si...
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| 主要な著者: | , , , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Taylor & Francis Group
2026-12-01
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| シリーズ: | Data Science in Science |
| 主題: | |
| オンライン･アクセス: | https://www.tandfonline.com/doi/10.1080/26941899.2026.2685344 |
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