Computational discovery of polymer electrolytes with Bayesian optimization and high-throughput molecular dynamics simulationsAntonia S Kuhn, Jurğis Ruža, KyuJung Jun, Pablo Leon, Rafael Gómez-BombarelliMatter (2026)
Research
We combine machine-learning models with atomistic simulations to understand ion transport in energy materials and to find new battery materials. The work spans inorganic crystals, amorphous solids, polymers, and liquids.
- AI-driven autonomous materials discovery for batteries
- Machine-learning interatomic potentials and accelerated simulation
- Ion transport mechanisms in superionic conductors
- Mechanistic decomposition of transport in liquid, polymer, and amorphous electrolytes
- High-throughput and machine-learning-guided materials discovery
AI-driven autonomous materials discovery for batteries
How can machine-learning models, simulations, and experiments be connected so that each round chooses the next material to make and test?
We build decision loops in which a model proposes candidate materials, simulations or experiments evaluate them, and the results update the model for the next round. The target is battery materials such as solid and polymer electrolytes, where the search space is too large to cover one sample at a time.
Methods
- Bayesian optimization and active learning
- High-throughput simulation as a surrogate for experiments
- Closed-loop coupling with automated synthesis and testing
Representative papers
Hierarchical high-throughput screening of alkaline-stable lithium-ion conductors combining machine learning and first-principles calculationsZhuohan Li*, KyuJung Jun*, Bowen Deng, Gerbrand CederCell Press Blue (2026)
Machine-learning interatomic potentials and accelerated simulation
How can simulations keep first-principles accuracy while reaching nanosecond time scales and nanometer length scales?
We develop, fine-tune, and test machine-learning interatomic potentials, and use generative models to accelerate molecular dynamics, so that transport in disordered and complex materials can be simulated directly.
Methods
- Universal and fine-tuned machine-learning interatomic potentials
- Generative models for accelerated dynamics
- Free-energy methods
Representative papers
CHGNet as a pretrained universal neural network potential for charge-informed atomistic modellingBowen Deng, Peichen Zhong, KyuJung Jun, …, Gerbrand CederNature Machine Intelligence, 5 (9) 1031–1041 (2023)
Flow matching for accelerated simulation of atomic transport in crystalline materialsJuno Nam, Sulin Liu, Gavin Winter, KyuJung Jun, Soojung Yang, Rafael Gómez-BombarelliNature Machine Intelligence, 1–11 (2025)
Systematic softening in universal machine learning interatomic potentialsBowen Deng, Yunyeong Choi, Peichen Zhong, …, KyuJung Jun, …, Gerbrand Cedernpj Computational Materials, 11 (1) 9 (2025)
The free energy landscape of Li- and Na-ion transport in nanoconfinement with machine learning interatomic potentialsSauradeep Majumdar, Swagata Roy, KyuJung Jun, Miguel Steiner, Rafael Gómez-BombarelliChemistry of Materials, 38 (8) (2026)
Ion transport mechanisms in superionic conductors
Why do some crystal structures conduct Li and Na ions orders of magnitude faster than others?
We study how the framework of a crystal (corner-sharing polyhedra, anion-group rotation and tilting, van der Waals gaps) sets the energy landscape for ion hops, and turn these findings into design rules for new solid electrolytes.
Methods
- Ab initio and machine-learning molecular dynamics
- Algorithms that detect correlated ion hops and anion-group motion
- Structure–property analysis across material families
Representative papers
Lithium superionic conductors with corner-sharing frameworksKyuJung Jun*, Yingzhi Sun*, Yihan Xiao, …, Gerbrand CederNature Materials, 1–8 (2022)
The nonexistence of a paddlewheel effect in superionic conductorsKyuJung Jun*, Byungju Lee*, Ronald L. Kam, Gerbrand CederProceedings of the National Academy of Sciences, 121 (18) e2316493121 (2024)
Diffusion mechanisms of fast lithium-ion conductorsKyuJung Jun, Yu Chen, Grace Wei, Xiaochen Yang, Gerbrand CederNature Reviews Materials, 1–19 (2024)
Exploring the soft cradle effect and ionic transport mechanisms in the LiMXCl4 superionic conductor familyKyuJung Jun*, Grace Wei*, Xiaochen Yang, Yu Chen, Gerbrand CederMatter, 102001 (2025)
Reply to Smith and Siegel: Most lithium hops in paddlewheel-claimed conductors occur without spatially and temporally correlated anion-group rotationsKyuJung Jun, Gerbrand CederProceedings of the National Academy of Sciences, 122 (9) e2423194122 (2025)
Mechanistic decomposition of transport in liquid, polymer, and amorphous electrolytes
Which microscopic events carry the charge in an electrolyte, and how much does each contribute to the measured conductivity?
We develop analysis methods that split the ionic conductivity from a simulation into contributions from single-ion hops, correlated hops, and vehicular motion. The same framework applies to inorganic crystals, liquids, and polymers.
Methods
- Event detection in molecular dynamics trajectories
- Decomposition of Onsager transport coefficients into additive contributions
- Large-scale classical and machine-learning molecular dynamics
Representative papers
Universal Framework for Decomposing Ionic Transport into Interpretable MechanismsKyuJung Jun*, Pablo A Leon*, Jurğis Ruža, Juno Nam, Rafael Gómez-BombarelliarXiv (2026)
Mechanistic Decomposition of Ion Transport in Amorphous Polymer Electrolytes via Molecular DynamicsPablo A Leon*, KyuJung Jun*, Kiarash Gordiz, Yang Shao-Horn, Rafael Gomez-BombarelliThe Journal of Physical Chemistry Letters, 12419–12427 (2025)
Benchmarking Classical Molecular Dynamics Simulations for Computational Screening of Lithium Polymer ElectrolytesJurğis Ruža, Pablo Leon, KyuJung Jun, Jeremiah Johnson, Yang Shao-Horn, Rafael Gómez-BombarelliMacromolecules (2025)
Mechanisms of Alkali Ionic Transport in Amorphous Oxyhalides Solid State ConductorsLuca Binci, KyuJung Jun, Bowen Deng, Gerbrand CederAdvanced Energy Materials (2026)
High-throughput and machine-learning-guided materials discovery
Which compositions are fast ion conductors and remain stable under operating conditions?
We screen large sets of candidate compounds for ionic conductivity and stability, and work with experimental groups to synthesize and test the predictions. Targets include solid electrolytes, mixed ionic–electronic conductors, and cathode additives.
Methods
- First-principles calculations (DFT)
- Machine-learning screening of large candidate sets
- Stability analysis (phase diagrams, electrochemical windows)
Representative papers
Nitride Lithium-ion Conductors with Enhanced Oxidative StabilityKyuJung Jun, Yihan Xiao, Wenhao Sun, Young-Woon Byeon, Haegyeom Kim, Gerbrand CederJournal of The Electrochemical Society, 171 (9) 090518 (2024)
Screening and Development of Sacrificial Cathode Additives for Lithium‐Ion BatteriesHaegyeom Kim*, KyuJung Jun*, Nathan Szymanski, …, Wanli YangAdvanced Energy Materials (2025)
Computational design of polaronic conductive Li-NASICON mixed ionic–electronic conductorsJiawei Lin, KyuJung Jun†, Gerbrand Ceder†Journal of Materials Chemistry A, 13 (21) 15659–15672 (2025)
Lithium Oxide Superionic Conductors Inspired by Garnet and NASICON StructuresYihan Xiao*, KyuJung Jun*, Yan Wang, Lincoln J. Miara, Qingsong Tu, Gerbrand CederAdvanced Energy Materials, 11 (37) 2101437 (2021)
