Liu Dongfei, Wu Jianzhong, Lu Diannan
Department of Chemical Engineering, Tsinghua University, Beijing 100084, People's Republic of China.
Department of Chemical and Environmental Engineering, University of California, Riverside, California 92521, USA.
J Chem Phys. 2023 Jul 28;159(4). doi: 10.1063/5.0153196.
Machine learning potentials (MLPs) are poised to combine the accuracy of ab initio predictions with the computational efficiency of classical molecular dynamics (MD) simulation. While great progress has been made over the last two decades in developing MLPs, there is still much to be done to evaluate their model transferability and facilitate their development. In this work, we construct two deep potential (DP) models for liquid water near graphene surfaces, Model S and Model F, with the latter having more training data. A concurrent learning algorithm (DP-GEN) is adopted to explore the configurational space beyond the scope of conventional ab initio MD simulation. By examining the performance of Model S, we find that an accurate prediction of atomic force does not imply an accurate prediction of system energy. The deviation from the relative atomic force alone is insufficient to assess the accuracy of the DP models. Based on the performance of Model F, we propose that the relative magnitude of the model deviation and the corresponding root-mean-square error of the original test dataset, including energy and atomic force, can serve as an indicator for evaluating the accuracy of the model prediction for a given structure, which is particularly applicable for large systems where density functional theory calculations are infeasible. In addition to the prediction accuracy of the model described above, we also briefly discuss simulation stability and its relationship to the former. Both are important aspects in assessing the transferability of the MLP model.
机器学习势（MLP）有望将第一性原理预测的准确性与经典分子动力学（MD）模拟的计算效率相结合。尽管在过去二十年中开发MLP取得了巨大进展，但在评估其模型可转移性和促进其发展方面仍有许多工作要做。在这项工作中，我们为石墨烯表面附近的液态水构建了两个深度势（DP）模型，模型S和模型F，后者具有更多的训练数据。采用并发学习算法（DP-GEN）来探索超出传统第一性原理MD模拟范围的构型空间。通过检查模型S的性能，我们发现对原子力的准确预测并不意味着对系统能量的准确预测。仅偏离相对原子力不足以评估DP模型的准确性。基于模型F的性能，我们提出模型偏差的相对大小和原始测试数据集的相应均方根误差，包括能量和原子力，可以作为评估给定结构模型预测准确性的指标，这特别适用于密度泛函理论计算不可行的大型系统。除了上述模型的预测准确性外，我们还简要讨论了模拟稳定性及其与前者的关系。两者都是评估MLP模型可转移性的重要方面。
