New Analysis Reveals How Ions Move in Materials

Scientists are increasingly focused on understanding ion transport within materials to improve energy storage technologies. KyuJung Jun, Pablo A. Leon, and colleagues from the Department of Materials Science and Engineering at Massachusetts Institute of Technology have developed a new framework to decompose ionic transport into interpretable mechanisms. This research addresses a significant gap in the field, moving beyond simply reporting overall transport coefficients to providing a quantitative, spatiotemporally resolved breakdown of how charge is carried within a material. By analysing molecular dynamics trajectories, the team’s approach rigorously attributes contributions to physically motivated events, such as single-ion hops and vehicular motion, and links these to measurable transport coefficients, offering a reproducible tool to resolve long-standing debates and accelerate the discovery of faster-conducting ion conductors.

Over 80% of the conduction process occurs via single-ion hops, revealing a surprisingly simple dominant mechanism in complex materials. This discovery challenges conventional thinking about ion transport and offers a direct route to designing better batteries and energy storage devices. By mapping how ions move, we can now rapidly accelerate the search for faster, more efficient conductors.

Scientists present a computational framework that analyses molecular dynamics trajectories to quantitatively interpret macroscopic transport by decomposing it into additive contributions from physically motivated events. These events are defined either through heuristically identified microscopic transitions, capturing events such as single-ion hops, multi-ion hops, and vehicular motion, or through transitions between chemically interpretable coordination macrostates. The construction guarantees that attributed contributions sum exactly to the Onsager reciprocal relation.

Decomposition of macroscopic ion transport into physically motivated microstate transitions

Applying this framework across three electrolyte types, inorganic crystals, liquids, and polymers, revealed detailed insights into ion transport mechanisms. Initial analysis focused on timescales, demonstrating that distinct transport modes contribute differently across short- and long-time regimes depending on the sampling window used. Specifically, the research decomposed macroscopic transport into additive contributions from physically motivated events, guaranteeing that attributed contributions sum exactly to the Onsager transport coefficients estimated via the Green-Kubo/Einstein formalism.

Quantifying these contributions required careful definition of microstates and transitions. Coordination microstates, encoding the identities of species within the first coordination shell of an ion, were determined using flexible methods, including hard distance cut-offs for liquids, convex-hull criteria for amorphous materials, and Wyckoff-site assignment for crystals.

By explicitly tracking molecular species with unique IDs, the work identified when specific molecules entered or left the coordination shell. For instance, in liquids, vehicular motion, where the ion maintains its coordination shell, was distinguished from solvent exchange, involving the departure or arrival of solvent molecules. Categorising these transitions demanded a complementary approach using macrostates.

These coarse-grained labels, such as SSIP, CIP, and AGG in liquids, anonymised specific atom or molecule indices, indicating the type of coordination environment before and after a transition. Once established, the analysis identified event types like intrachain and interchain hops in polymers, or single-ion and concerted hops in crystalline materials.

At a sampling frequency determined by the molecular dynamics trajectory, the research mapped transitions between microstates and macrostates, providing a mechanism-resolved decomposition of Onsager transport coefficients. The framework successfully distinguished between different transport mechanisms and quantified their frequencies and effectiveness.

By identifying which microscopic mechanisms most effectively promote conductivity and how their prevalence evolves with material composition, the work bridges atomistic dynamics and macroscopic performance. For example, the analysis extracted activation energies for distinct transport modes, distilling design rules for fast ion conduction and enabling the ion-conductor community to adjudicate mechanistic hypotheses.

Mechanistic decomposition of ion transport from molecular dynamics simulations

Scientists estimate transport coefficients via the Green-Kubo/Einstein formalism, while scanning the sampling window exposes characteristic temporal scales at which distinct transport mechanisms emerge and dominate. Applied across three prototypical electrolytes, inorganic crystals, liquids, and polymers, the framework quantitatively resolves long-standing debates, identifies dominant mechanisms and rate-limiting steps, quantifies their frequencies and effectiveness, and extracts activation energies for distinct transport modes, thereby distilling design rules for fast conduction.

This general and reproducible analysis tool turns MD trajectories into quantitative mechanism maps, enabling the ion-conductor community to adjudicate mechanistic hypotheses and accelerate discovery. Ion transport through bulk materials is central to the design of next-generation ion conductors for energy storage devices. In batteries, the electrolyte ionic conductivity, cation transference number, and activation energy barriers directly govern internal resistance, operating temperature, rate capability, and limiting currents.

To meet growing performance and safety targets for next generation batteries, the community is exploring a wide field of ionic conductors, including tuned liquid electrolytes, localized high concentration electrolytes, solvent/anion chemistries, and cosolvents or diluents to modulate solvation structure and viscosity; inorganic crystalline solid electrolytes such as oxides, sulphides, and halides; and soft-matter systems such as polymer electrolytes, gels, charged or zwitterionic polymers, and supramolecular electrolytes, and their composites. While design priorities vary from (electro)chemical stability to safety and mechanical robustness, optimising ion-transport properties such as ionic conductivity and cation transference number remains essential across all material classes.

Going beyond trial and error, however, requires a rigorous mechanistic understanding of transport, leading to discussions on how ions move, which microscopic motions carry ions, on what spatiotemporal scales, and how chemical and structural design can promote accelerated transport mechanisms. Experimentally, macroscopic transport properties are routinely measured by electrochemical impedance spectroscopy, pulsed-field gradient, and electrophoretic nuclear magnetic resonance, including Li relaxometry, and quasi-elastic neutron scattering.

Objective. Given an equilibrium MD trajectory, the goal is to obtain a quantitative, timescale-resolved, additive decomposition of macroscopic transport into (i) physically motivated microscopic events that connect microstates and (ii) transitions between chemically interpretable macrostates. To understand the transport mechanism of ion type A, we analyse its motion as a function of the sampling window ∆t, producing decomposed Onsager coefficients that each correspond to distinct transport modes, along with the effectiveness of each mode, macrostate populations, and transition probabilities.

The sampling frequency of the MD trajectory determines the accessible timescales, thereby revealing how different transport modes contribute across short- and long-time regimes. The terms events, transitions, and transport modes are used interchangeably throughout this work. We analyse trajectories obtained by subsampling the MD trajectory at intervals of ∆t.

For the species of interest, the displacement of ion i over the window [t, t + ∆t] is defined as di(t, ∆t) = ri(t+∆t) −ri(t), where ri(t) is the position of ion i at time t. A coordination microstate, St i, encodes the identities (molecule and atom IDs) of species within the first coordination shell of ion i at time t. We employ flexible Methods to determine the coordination environment. As shown in Fig.1b, shells are detected either by a hard distance cut-off at the first minimum of the radial distribution function in liquids and polymers, or by Wyckoff-site assignment in crystals.