Interaction of Magnesium Ion and Acetate Anion in Bulk Water:Toward High-Level Machine Learning Potential  

镁离子与乙酸根在水溶液中的相互作用研究:走向高精度机器学习势能模拟

作　　者：Jiaying Gu Jin Xiao Xingyu Wu Xi Zhu Huimin Chen John ZHZhang Tong Zhu Ya Gao Zhixiang Yin 顾嘉滢;肖进;吴星雨;朱希;陈惠敏;张增辉;朱通;高雅;殷志祥(上海工程技术大学数理与统计学院,上海201620;华东师范大学化学与分子工程学院,上海分子治疗与新药创制工程技术研究中心,上海200062;中国科学院.定量工程生物学重点实验室,深圳合成生物学创新研究院,中国科学院深圳先进技术研究院,深圳518055;上海纽约大学计算化学联合研究中心,上海200062;美国纽约大学化学系,纽约10003;山西大学光学协同创新中心,太原030006;上海市心脑血管非编码RNA成药性前沿科学研究基地(培育),上海201620)

机构地区：[1]School of Mathematics,Physics and Statistics,Shanghai University of Engineering Science,Shanghai 201620,China [2]Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development,School of Chemistry and Molecular Engineering,East China Normal University,Shanghai 200062,China [3]CAS Key Laboratory of Quantitative Engineering Biology,Shenzhen Institute of Synthetic Biology,Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China [4]NYU-ECNU Center for Computational Chemistry at NYU Shanghai,Shanghai 200062,China [5]Department of Chemistry,New York University,New York 10003,USA [6]Collaborative Innovation Center of Extreme Optics,Shanxi University,Taiyuan 030006,China [7]Shanghai Frontiers Science Research Center for Druggability of Cardiovascular Noncoding RNA,Shanghai 201620,China

出　　处：《Chinese Journal of Chemical Physics》2025年第1期95-101,I0056,共8页化学物理学报(英文)

基　　金：supported by the National Natural Sci-ence Foundation of China(No.22373065,No.62072296,No.22222303,No.22173032,No.21933010);the Nation-al Key R&D Program of China(No.2023YFF1204903);NYU-ECNU Center for Computational Chemistry at NYU Shanghai,the Opening Project of Shanghai Frontiers Science Research Center for Druggability of Cardiovascular noncoding RNA.

摘　　要：Metal ions play crucial roles in various biologi-cal functions,in-cluding maintain-ing homeostasis,regulating mus-cle contraction,and facilitating enzyme catalysis.However,accurately simulating the interaction between metal ions and amino acid side chain analogs using high-level wave function theories remains challenging due to the significant computational costs involved.In this study,deep potential molecular dynamics(DeePMD)simulation was employed to investigate the solvation structure of the Mg^(2+)-Ac^(−)ion pair in aqueous solution.To address the computational bottleneck associated with expensive quan-tum mechanics(QM)methods,the Deep Kohn-Sham(DeePKS)approach was utilized,which allows us to generate highly accurate self-consistent energy functionals while significantly re-ducing computational costs.The root mean square error and mean absolute error of energies and atomic forces indicate close agreement between DeePKS predictions and QM strongly constrained and appropriately normed(SCAN)calculations.Moreover,the neural network potential(NNP)generated using the SCAN-level dataset predicted by DeePKS exhibits high-er accuracy compared to previous work,which employed a moderate BLYP functional.The potential of mean force for the Mg^(2+)-Ac−system was further examined,revealing a prefer-ence for monodentate coordination of Mg^(2+)with a~5.8 kcal/mol energy barrier between bidentate and monodentate geometries.Overall,this work provides a comprehensive,precise,and reliable methodology for investigating metal ions’properties in aqueous solutions.金属离子在许多生物功能中扮演至关重要的角色,例如维持体内平衡、调控肌肉收縮和促进酶催化作用.尽管如此,使用高阶波函数理论精确模拟金属离子与氨基酸侧链类似物之间的相互作用由于涉及高昂的计算成本而面临挑战.本研究采用了深度势能分子动力学模拟技术,探究了水溶液中Mg^(2+)和Ac^(-)离子对的溶剂化结构.为了克服传统量子力学方法高成本的计算瓶颈,引入了深度Kohn-Sham(DeePKS)方法,该方法能够在大幅降低计算成本的同时,生成高精度的自洽能量函数.能量及原子力的均方根误差和平均绝对误差结果显示,DeePKS的预测与传统量子力学的强约束并适当规范化计算非常吻合.此外,使用DeePKS预测的强约束并适当规范化级数据集生成的神经网络势在准确性上超越了之前采用BLYP泛函的研究.进一步分析了Mg^(2+)和Ac^(-)体系的平均力势,发现Mg^(2+)倾向于单齿配位,而双齿与单齿结构之间存在约5.8 kcal/mol的能量障碍.总之,本研究提供了一种全面、精确并且可靠的方法,用于探索水溶液中金属离子的性质.

关 键 词：Molecular dynamics simulation Umbrella sampling Neural network potential Machine learning 

分 类 号：O641.3[理学—物理化学] TP181[理学—化学]