Abstract: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 simulations, the computational cost constrains the applicable domain in practice. Machine learning models, with the potential to learn the complex emergent behaviors governing atomistic transitions as a fast surrogate model, have great promise to predict transitions with a vastly reduced computational cost. Here, we demonstrate how transformers can be trained to predict atomistic transitions in nano-clusters. We show how we evaluate physical validity of the predictions and how a multitude of additional, different microstates can be generated by slightly varying the data provided to the model.
| Comments: | Presented at the 2025 Conference on Data Analysis (CoDA), February 25-28, Santa Fe, New Mexico |
| Subjects: | Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG) |
| Cite as: | arXiv:2603.06526 [cond-mat.mtrl-sci] |
| (or arXiv:2603.06526v2 [cond-mat.mtrl-sci] for this version) | |
| https://doi.org/10.48550/arXiv.2603.06526 arXiv-issued DOI via DataCite |
From: Henry Tischler [view email]
[v1]
Thu, 5 Mar 2026 03:09:58 UTC (70,583 KB)
[v2]
Wed, 29 Apr 2026 18:43:23 UTC (41,605 KB)
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