Distilling Large-Scale Machine Learning Interatomic Potentials to Predict Long-Timescale Electrolyte Transport Properties
Authors: Ishan Amin, Yue Jian, Nitesh Kumar, Christine Zhang, Jianwei Lai, Casey S. Mezerkor, Aaron M. Melemed, Jonathan R. Thurston, Neil P. Dasgupta, J. David Bazak, Ethan J. Crumlin, Samuel Blau, Aditi S. Krishnapriyan
Organizations: Department of Computer Science, University of California, Berkeley, Berkeley, California 94720, USA · Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, California 94720, USA · Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA · Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA · Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99354, USA · Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA
Accurately predicting the transport properties of liquid electrolytes is critical for next-generation battery design and requires force fields that are both quantum-mechanically accurate and fast enough for long-timescale simulation. Recent large-scale pretrained machine learning interatomic potentials (MLIPs) approach density-functional-theory accuracy, but remain too slow to access the necessary timescales to converge transport properties. We provide a knowledge distillation procedure to transfer the accuracy of a large-scale "teacher" MLIP into fast, system-specific "students", without any additional ab initio data. Our distilled models run ~40x faster, reproduce teacher-predicted structural observables, and produce stable 20 ns trajectories in days, rather than the months the teacher would require. Across a diverse set of sodium- and lithium-ion electrolytes, student-predicted self-diffusivities, ionic conductivities, and shear viscosities track experiment and outperform classical force-field predictions, establishing distillation as a practical route to accurate, long-timescale simulations necessary for high-throughput electrolyte screening.
Figures & tables
Figure 1: Teacher–student distillation workflow for converged electrolyte transport properties. The workflow distills an accurate but slow teacher potential into a fast, system-specific student that can be run long enough to converge transport coefficients. (1) Data generation and distillation. The teacher potential first runs a short reference trajectory, from which training configurations are sampled. To broaden coverage beyond near-equilibrium structures, half of the sampled configurations are rattled with Gaussian noise and relabeled with the teacher to provide consistent energy, force, and stress targets. The student potential is then trained on this mixed dataset of both perturbed and unperturbed samples. (2) Production simulations. Once trained, the student replaces the teacher for production molecular dynamics, running ∼40× faster and reaching trajectory lengths of 20 ns. (3) Convergence and validation. The 20 ns trajectories allow transport observables to converge. Transport coefficients start out noisy at short timescales (1 ns), but converge near experimental values once the trajectory is sufficiently long.
Figure 2: Distilled PaiNN potentials reproduce the static observables of the UMA-OMol teacher at short timescales. (a) Radial distribution functions of four electrolyte systems across multiple correlation pairs, obtained from 1 ns trajectories at 293 K, with both the teacher model (UMA-OMol) and the student (PaiNN) displayed. Each PaiNN line is the mean over five independent 1 ns replicas, where the shading indicates the ±1 s.d. replica spread. The teacher curve falls within the shaded band for almost every pair and system, indicating that the teacher lies within the student’s per-replica noise and that the student accurately reproduces teacher behavior. (b) Parity plot of simulated versus experimentally measured mass density, where each dot denotes a different electrolyte system. Both UMA-OMol and PaiNN show a systematic upward bias against experiment, but UMA-OMol and PaiNN coincide for almost every system, so distillation adds little error of its own. The individual densities for each electrolyte are listed in Sec. . (c) For a 20 ns NPT 1 M NaOTf in TEGDME simulation, we evaluate force mean absolute error (MAE) and stress MAE relative to UMA-OMol at 20 ps intervals by using UMA-OMol to compute single-point estimates. Light traces are per-frame errors, while bold curves show the ∼ 500 ps running average. Both errors stay low, with no upward drift over 20 ns: a linear fit changes the force MAE by < 0.2%, and the stress MAE decreases slightly over time. Similar plots for the production runs are shown in Sec. .
Figure 3: Ionic conductivity and shear viscosity vs. experiment at 298 K, for the distilled student models and the OPLS-AA force field. Each point is the mean over four independent 20 ns replicas, and error bars show ±1 s.d. across the replicas. (a,b) Ionic conductivity σ for the PaiNN student and OPLS-AA. Conductivity is only computed for the concentrated ( ≥ 0.5 M) systems, since at 0.1 M the box holds too few charge carriers to converge the collective estimator. The student roughly halves the median absolute percentage deviation of OPLS-AA (MdAPD; 36% vs. 67%), with a comparable log–log correlation. (c,d) Viscosity η against experiment for the two force fields. Again the student tracks experiment much more closely than OPLS-AA, with an MdAPD less than half that of OPLS-AA and a log–log fit slope closer to one.
Figure 4: Self-diffusivities vs. experiment at 298 K, for the distilled students and the OPLS-AA force field. Self-diffusivities of the cation D+ (a,d) , anion D− (b,e) , and solvent D0 (c,f) , for the PaiNN student (top row) and OPLS-AA (bottom row), where each point is the mean over four independent 20 ns replicas (error bars, ±1 s.d. across the replicas), with the Yeh–Hummer finite-size correction applied [ 54 ] . The student outperforms OPLS-AA for every species, both in MdAPD and in log–log fit slope.
Department of Materials Science and Engineering, National University of Singapore, Singapore · Department of Physics, National University of Singapore, Singapore
Department of Chemistry, UC Berkeley, California 94720, United States · Bakar Institute of Digital Materials for the Planet, UC Berkeley, California 94720, United States · Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, United States
Engineering Laboratory, University of Cambridge, Trumpington St, Cambridge, UK · University of Bayreuth, Bavarian Center for Battery Technology (BayBatt), Bayreuth, Germany · Fritz-Haber-Institute of the Max-Planck-Society, Berlin, Germany