用神经网络连续建模化学反应路径，更灵活高效。
Implicit Neural Representations for Chemical Reaction Paths
- 用神经网络参数化反应路径，优化时忽略切向能量梯度
- 在复杂系统中优于NEB方法，可处理多路径与非物理初猜
- 单个网络可学习并泛化到未见体系，具通用潜力
我们证明神经网络可通过优化表示最小能量路径为连续函数，提供一种灵活替代离散路径搜索方法（如Nudged Elastic Band, NEB）。该方法通过损失函数舍弃切向能量梯度，实现过渡态的即时估计。首先在二维势能面上验证方法有效性，随后在具有挑战性的原子体系中展示其优势：（i）对不良初始猜测仍能生成合理路径；（ii）可识别多个竞争性反应路径；（iii）适用于复杂多步反应机制。结果表明该方法高度灵活：例如，仅调整优化过程中的采样策略即可帮助摆脱局部最优解。最后，在低维情形下，单一神经网络可从已有路径中学习并泛化至未见系统，展现出构建通用反应路径表示的前景。
原文摘要 · Abstract (English)
We show that neural networks can be optimized to represent minimum energy paths as continuous functions, offering a flexible alternative to discrete path-search methods such as Nudged Elastic Band (NEB). Our approach parameterizes reaction paths with a network trained on a loss function that discards tangential energy gradients and enables instant estimation of the transition state. We first validate the method on two-dimensional potentials and then demonstrate its advantages over NEB on challenging atomistic systems where (i) poor initial guesses yield unphysical paths, (ii) multiple competing paths exist, or (iii) the reaction follows a complex multi-step mechanism. Results highlight the versatility of the method: for instance, a simple adjustment to the sampling strategy during optimization can help escape local-minimum solutions. Finally, in a low-dimensional setting, we demonstrate that a single neural network can learn from existing paths and generalize to unseen systems, showing promise for a universal reaction path representation.
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