Supplementary data (CC BY 4.0): A reactive neural network framework for water-loaded acidic zeolites

<strong>Content (Creative Commons Attribution 4.0 International</strong><strong>):</strong> Energy and force data (ASE trajectory files) calculated at the DFT (SCAN+D3(BJ)), NNP and ReaxFF level for error statistics of the NNP generalization tests Trajectories of NNP level MD simulations (ASE trajectory files) for all generalization tests Trajectories for all NEB calculations Trained Neural Network Potentials NNPs including the Δ-learned model (SchNetPack, version 1.0) are available in another Zenodo repository version under CC BY-NC-SA 4.0 license (https://doi.org/10.5281/zenodo.8139369) for non-commercial use only.内容（采用知识共享署名4.0国际许可协议）：为开展神经网络势（Neural Network Potential, NNP）泛化测试的误差统计分析，本数据集包含在密度泛函理论（Density Functional Theory, DFT，采用SCAN+D3(BJ)泛函）、神经网络势以及ReaxFF理论水平下计算得到的能量与力数据（ASE轨迹文件）；所有泛化测试所用的神经网络势水平分子动力学（Molecular Dynamics, MD）模拟轨迹文件（ASE轨迹格式）；以及所有爬坡弹性带（Nudged Elastic Band, NEB）计算所需的轨迹文件。包含Δ学习模型（基于SchNetPack 1.0版本构建）的已训练神经网络势，可在另一采用知识共享署名-非商业性使用-相同方式共享4.0国际许可协议的Zenodo仓储中获取（链接：https://doi.org/10.5281/zenodo.8139369），仅允许非商业性使用。