用神经网络加速能量路径计算，让复杂系统模拟从数周缩至数小时。
Efficient Nudged Elastic Band Method using Neural Network Bayesian Algorithm Execution
- 用神经网络联合学习能量面与最低能量路径，动态选点优化计算。
- 在典型系统中减少10到100倍的力与能量评估，精度损失极小。
- 可扩展至超百维系统，适合材料与生物分子设计等高复杂度研究。
发现亚稳态之间的最低能量路径（MEP）对催化剂和生物分子设计等科学任务至关重要。然而，标准的弹性带法（NEB）需要数百至数万次计算密集型模拟，使复杂系统应用成本过高。本文提出神经网络贝叶斯执行框架（NN-BAX），通过联合学习能量景观与MEP，逐步精调基础模型，并主动选择有助于提升路径的样本。在Lennard-Jones和嵌入原子模型（EAM）系统上测试表明，该方法将能量与力评估次数降低一到两个数量级，且路径精度损失可忽略，同时可拓展至超过100维系统。这项工作有望突破科学系统中MEP发现的计算瓶颈，使原本需数周的计算在数小时或数天内完成，且精度损失微小。
原文摘要 · Abstract (English)
The discovery of a minimum energy pathway (MEP) between metastable states is crucial for scientific tasks including catalyst and biomolecular design. However, the standard nudged elastic band (NEB) algorithm requires hundreds to tens of thousands of compute-intensive simulations, making applications to complex systems prohibitively expensive. We introduce Neural Network Bayesian Algorithm Execution (NN-BAX), a framework that jointly learns the energy landscape and the MEP. NN-BAX sequentially fine-tunes a foundation model by actively selecting samples targeted at improving the MEP. Tested on Lennard-Jones and Embedded Atom Method systems, our approach achieves a one to two order of magnitude reduction in energy and force evaluations with negligible loss in MEP accuracy and demonstrates scalability to >100-dimensional systems. This work is therefore a promising step towards removing the computational barrier for MEP discovery in scientifically relevant systems, suggesting that weeks-long calculations may be achieved in hours or days with minimal loss in accuracy.
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