The [EMIm]+Cl–+AlCl3 ion liquid is a promising prototype electrolyte for aluminum-ion batteries (AIBs). Its ionic transport behavior involves multiple mobile species (Al3+, AlCl3, [AlCl4]– and [Al2Cl7]–), with ion migration mechanisms and conversion reactions among these species unsolved experimentally. This complexity results in heterogeneous ion migration mechanisms and sluggish diffusion kinetics, which cannot be accurately and reliably captured by the traditional first-principles molecular dynamics (FPMD) simulations within the very limited time duration (tens of ps) and relatively small modelling structure (less than 103 atoms). The classic molecular dynamics simulations based on various force fields are also scarce for studying and predicting the atomic structure evolution and ion diffusion dynamics of the complex electrolyte system such as ion liquids. In this work, a deep neural network interatomic potential (DP-potential) is developed through machine learning techniques, combining first-principles accuracy with classical molecular dynamics efficiency, to systematically investigate various chemical and physical properties for [EMIm]+Cl–+AlCl3 ion-liquid at finite temperatures. Training and validating of DP potential for [EMIm]+Cl–+AlCl3 ion liquid are implemented with a two-stage protocol, including the primary training stage and the refining stage. Before initiating the two training stages, a series of first-principles molecular dynamics (FPMD) simulations is performed for [EMIm]+Cl–+AlCl3 ion liquids with different molar ratios (1.0, 1.3, 1.5, 1.7 and 2.0) and equilibrium densities (1.09—1.56 g/cm3) at finite temperatures (300 K and 400 K), resulting in a highly diverse training datasets spanning a board range of chemical compositions and densities during the primary training stage for DP potential. Then, the trained DP-potential is employed to conduct long-timescale classic molecular dynamics simulations by using LAMMPS program for the [EMIm]+Cl–+AlCl3 ion liquids to produce the atomic configurations that either show significant errors in the calculated atomic forces and total energies or exhibit the unusual atomic evolution before crashing. Those highly extrapolated atomic configurations are merged with the initial training datasets to reoptimize the DP potential in the second refining stage. Through this two-stage training approach, a deep learning neural network interatomic potential with high accuracy is successfully constructed, achieving an energy prediction error of 5×10–4 eV/atom and a force prediction error of 5×10–2 eV/Å. The reliability of the finally obtained machine learning potential is further validated through a systematic comparison of radial distribution functions (RDF) for some representative atomic pairs such as C—N, C—H, Al—Cl and Cl—H, obtained from both DP-MD and FPMD, demonstrating excellent consistency for the results from the two methods. The DP-MD simulations are systematically carried out to investigate vibrational spectrum and Al3+ diffusion dynamics as well as possible conversion reactions among molecular or ionic species (Al3+, AlCl3, [AlCl4]– and [Al2Cl7]–) in [EMIm]+Cl–+AlCl3 ion liquids within 104 atoms at finite temperatures. From the calculated vibrational density of states (VDOS), it can be seen that the VDOS of [EMIm]+Cl–+AlCl3 ion liquid can be approximated as a simple superposition of the vibrational spectra of individual species ([EMIm]+, [AlCl4]–, and [Al2Cl7]–), with H related vibrational modes dominating above 90 THz and the Al—Cl modes dominating below 20 THz. At 300 K, DP-MD predicts that regardless of the chemical compositions, the diffusion coefficient of Al3+ remains around 4 × 10–7 cm2/s at 300 K and the estimated diffusion activation energy is about 0.20 eV, which is very close to the experimental measurement value (0.15 eV). In addition, the calculated ionic conductivity of [EMIm]+Cl– + AlCl3 at room temperature is 27.37 mS/cm, with a deviation of only 18.2% from the experimental value (23.15 mS/cm). Notably, two different Al3+ diffusion mechanisms are identified in [EMIm]+Cl–+AlCl3 ion liquid: 1) direct migration processes conducted solely by molecular species including [AlCl4]– and [Al2Cl7]–, and 2) the migration of the neutral AlCl3 molecule mediated with two neighboring [AlCl4]– anions through the conversion reaction between [Al2Cl7]– and AlCl3+[AlCl4]– moieties. Furthermore, first-principles calculations on the probable dissociation pathways of [Al2Cl7]– revealed from DP-MD predict a reaction energy barrier height of 0.49 eV for the AlCl3 transferring between two [AlCl4]– anions with an increased reaction probability from 0.00047 events/(ps·Al3+) at 1∶1.3 molar ratio to 0.00347 events/(ps·Al3+) at 1∶1.75 molar ratio. Overall, a highly efficient and reliable workflow to train and validate the deep neural network interatomic potential for complex electrolyte system is successfully proposed, such as [EMIm]+Cl–+AlCl3 ion liquids, thus providing a more comprehensive investigation of Al3+ transport mechanisms in ionic liquid electrolytes for aluminum-ion batteries. In conclusion, this work can further advance the application of machine learning-based potentials in simulating electrolyte systems characterized by complex molecular architectures and sluggish diffusion dynamics.
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