Published in In preparation, 2025
Applies machine learning interatomic potentials to predict and characterize solid-state interfaces in sodium-ion battery systems, expanding the MLIP-based interface modeling framework to Na chemistries.
Recommended citation: Li, B., Jeon, H., Persson, K.A. "Machine Learning-Aided Prediction of Na Solid-State Interfaces." In preparation.
Published in In preparation, 2025
A collaborative review on how machine learning interatomic potentials are transforming our ability to model, understand, and design next-generation solid-state battery materials and interfaces.
Recommended citation: Phuthi, M.K., Wei, G., Li, B., Majumdar, S., Kolluru, V.S.C., Kumar, N., Blau, S., Canepa, P., Chan, M.K.Y., Gomez-Bombarelli, R., Persson, K.A., Ceder, G., Ong, S.P. "The Role of Machine Learning Interatomic Potentials in Developing the Next Generation of Solid-State Batteries." In preparation.
Published in arXiv, 2025
Develops a generalized framework for studying solid-state reaction interfaces using machine-learned interatomic potentials, extending atomistic simulations to a broad class of battery-relevant interface chemistries.
Recommended citation: Li, B., Karan, V., Persson, K.A. (2025). "Towards an Atomistic Understanding of Solid-State Reaction Interfaces Using Machine-Learned Interatomic Potentials." arXiv. DOI: arxiv.2511.20976.
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Published in The Journal of Physical Chemistry C, 2025
Uses machine-learned interatomic potentials to perform atomistic molecular dynamics simulations of SEI formation at the Li/Li₇P₃S₁₁ interface, revealing reaction mechanisms and decomposition pathways critical for solid-state battery design.
Recommended citation: Li, B., Karan, V., Kaplan, A.D., Wen, M., Persson, K.A. (2025). "An Atomistic Study of Reactivity in Solid State Electrolyte Interphase Formation for Li/Li7P3S11." The Journal of Physical Chemistry C. Accepted.
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Published in Digital Discovery, 2025
Describes Atomate2, a modular and extensible workflow library for high-throughput materials science calculations, enabling automated DFT, ML potential training, and multi-fidelity simulations at scale.
Recommended citation: Ganose, A.M., Sahasrabuddhe, H., Asta, M., Beck, K., Biswas, T., Bonkowski, A., ..., Li, B., ..., Jain, A. (2025). "Atomate2: Modular Workflows for Materials Science." Digital Discovery, 4(7), 1944–1973.
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Published in Chemistry of Materials, 2024
Presents a cellular automaton simulation framework for predicting phase evolution during solid-state reactions, enabling high-throughput screening of reaction pathways in battery materials and beyond.
Recommended citation: Gallant, M.C., McDermott, M.J., Li, B., Persson, K.A. (2024). "A Cellular Automaton Simulation for Predicting Phase Evolution in Solid-State Reactions." Chemistry of Materials, 37(1), 210–223.
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Published in Physical Review B, 2021
Develops a framework for quantifying multipoint ordering in alloy systems using cluster expansion methods, enabling more accurate characterization of complex ordering phenomena in Pt–Pd and related alloys.
Recommended citation: Goff, J., Li, B., Sinnott, S., Dabo, I. (2021). "Quantifying Multipoint Ordering in Alloys." Physical Review B, 104, 054109.
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