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Phys.org — Technology · September 11, 2026

Machine learning uncovers how battery interphases can boost lithium-ion transport

ChemistryMaterials ScienceChemical Engineering
THE AI ANGLE
Accelerating quantum-accurate molecular dynamics simulations across million-atom scales

Researchers at Lawrence Livermore National Laboratory developed machine-learning-accelerated molecular dynamics simulations trained on quantum-mechanical data to study battery interphases at the scale of millions of atoms over nanosecond durations. The study resolved a longstanding puzzle by demonstrating that while bulk lithium fluoride has intrinsically poor conductivity, its mixture with other species within the interphase creates viable pathways for lithium-ion transport. These findings provide actionable strategies for materials scientists and chemical engineers to rationally design interphases to improve electrochemical cell performance, safety, and longevity.

THE TEACHING ANGLE
Instructors can explore the contrast between bulk and interfacial properties by examining how an intrinsically poor conductor like bulk lithium fluoride can enable ion transport when heterogeneously mixed inside an operating battery interphase.

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