Bryant Y. Li

Bryant Y. Li

Ph.D. Candidate in Materials Science | Researching Solid-State Batteries and Machine Learning Applications | Materials informatics Expert

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portfolio

publications

Quantifying Multipoint Ordering in Alloys

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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A Cellular Automaton Simulation for Predicting Phase Evolution in Solid-State Reactions

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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Atomate2: Modular Workflows for Materials Science

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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An Atomistic Study of Reactivity in Solid State Electrolyte Interphase Formation for Li/Li₇P₃S₁₁

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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Towards an Atomistic Understanding of Solid-State Reaction Interfaces Using Machine-Learned Interatomic Potentials

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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The Role of Machine Learning Interatomic Potentials in Developing the Next Generation of Solid-State Batteries

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.

Machine Learning-Aided Prediction of Na Solid-State Interfaces

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.

talks

Awaruite and Tetrataenite Driven NH₃ Synthesis

Published:

Presented findings on catalytic ammonia production on Fe–Ni alloy clusters (Awaruite and Tetrataenite) as a mechanism relevant to the origins of life in the Archean Era. Classical potentials (Finnis–Sinclair, Lennard–Jones, Sutton–Chen) were used to model cluster thermodynamic stability and predict adsorption behavior of nitrogen and hydrogen species.

Machine Learning Aided Atomistic Modeling of Solid State Electrolyte Interphase Formation for Li/Li₇P₃S₁₁

Published:

Invited oral presentation on the application of machine learning interatomic potentials to atomistically model solid-electrolyte interphase (SEI) formation in sulfide-based solid-state batteries. Presented the end-to-end workflow from DFT data generation to MLIP training, large-scale MD simulation, and structural analysis of interfacial decomposition products.

teaching

Mathematics Tutor (100–200 Level)

Tutoring, Penn State Learning, The Pennsylvania State University, 2020

Provided one-on-one and group tutoring for 11 hours per week across a range of undergraduate mathematics courses, including Calculus I–III, Ordinary and Partial Differential Equations, and Linear Algebra. Adapted teaching methods to diverse learning styles and facilitated collaborative problem-solving sessions.

Teaching Assistant — Introduction to Materials Laboratory

Laboratory Course, Department of Materials Science and Engineering, The Pennsylvania State University, 2020

Led undergraduate laboratory sessions covering standard materials characterization techniques. Instructed students in optical microscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD). Assisted in grading lab reports and providing constructive feedback to improve technical writing and data analysis skills.