用Transformer模型快速预测纳米团簇的原子跃迁路径。
Predicting Atomistic Transitions with Transformers
- 将Transformer用于学习原子跃迁的复杂规律，替代传统高耗时模拟。
- 模型可生成多种微状态，且预测结果符合物理规律。
- 适合材料科学中需要高效探索原子结构变化的研究者。
材料与表面的原子跃迁路径准确知识对众多材料科学问题至关重要。然而，传统模拟方法寻找这些跃迁路径极为耗时。即使采用大规模加速材料模拟，计算成本仍限制了实际应用范围。机器学习模型有望通过学习原子跃迁的复杂涌现行为，作为快速替代模型，大幅降低计算成本。本文展示如何训练Transformer模型来预测纳米团簇中的原子跃迁，并验证预测结果的物理合理性。此外，通过轻微改变输入数据，模型还能生成大量不同的微观状态。
原文摘要 · Abstract (English)
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.
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