Machine learning (ml)-accelerated first-principles prediction method for hydration structure of acid radical anion

A machine learning (ML)-accelerated first-principles prediction method for a hydration structure of an acid radical anion, comprising the following steps:

• S1:
• constructing and optimizing an anion hydration structure M_mH2O;
• S2:
• perturbing an optimized anion hydration structure to generate a training dataset;
• S3:
• conducting a ML force field training on the training dataset to establish ML models;
• S4:
• conducting a molecular dynamics simulation on the ML models, and identifying atomic structures with a force deviation within a preset range as candidate configurations;
• S5:
• merging a validated candidate configuration into a training set for a subsequent iteration to further refine and train the ML models until the ML models converge, thereby generating an accurate deep potential (DP) model;
• and
• S6:
• conducting a ML-accelerated molecular dynamics simulation on the accurate DP model to ultimately acquire the hydration structure of the acid radical anion;
• wherein in the step S1, a method for constructing the anion hydration structure M_mH2O comprises:
• creating a periodic simulation box, placing an acid radical anion M at a center of the periodic simulation box, uniformly and randomly distributing m H2O around the acid radical anion M, and placing an alkali metal cation at an edge of the periodic simulation box to balance a charge, thereby completing a construction of an initial anion hydration structure M_mH2O, wherein m is above 40;
• before constructing the anion hydration structure M_mH2O, the ML-accelerated first-principles prediction method further comprises a pre-optimization, comprising:
• preparing a def2-TZVP, def2-SVP, def2-QZVP, def2-TZVPP, cc-pVDZ-PP, or aug-cc-pVDZ-PP basis set through an ωB97XD, B3LYP, or PBE functional;
• and pre-optimizing a simple hydration structure M_nH2O formed by n H2O in a sternest layer around the acid radical anion M to acquire a stern layer hydration structure of the acid radical anion M, wherein n is not more than 10;
• uniformly and randomly distributing a remaining (m-n) H2O around the initial anion hydration structure M_nH2O, thereby completing the construction of the initial anion hydration structure M_mH2O;
• and optimizing a constructed anion hydration structure through a Vienna ab initio simulation package;
• and
• in the step S2, the perturbing comprises:
• changing an atomic coordinate position and a size of the periodic simulation box to generate A different perturbed structures, each of the A different perturbed structures being in a canonical ensemble (NVT ensemble) at 298.15 K;
• and recording B frames for each configuration at a time step of 0.5-1.5 fs, and conducting a short-term ab initio molecular dynamics (AIMD) simulation to generate a training dataset of A×B frames as a basic training data for a DeepMD model, wherein A is 15-30;
• and B is 15-25.