Multiscale Simulation and Modeling Techniques for Next-Generation Nanomaterials in Energy Storage
A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Energy and Catalysis".
Deadline for manuscript submissions: 5 June 2026 | Viewed by 24
Special Issue Editor
Interests: nanomaterials; machine learning; artificial intelligence; energy storage; molecular dynamics; supercapacitor; electrochemical capacitors; energy materials; fuel cells
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Electrochemical energy storage technologies such as lithium-ion, sodium-ion, multivalent batteries, supercapacitors, and solid-state devices increasingly rely on complex nanostructured materials to achieve high energy and power density, fast charging, and long cycle life. At these small length scales, device performance is governed by strongly coupled phenomena—electronic structure, ion transport, mechanical deformation, phase transformations, and interfacial reactions—that span from the atomistic to the electrode and cell level. Multiscale simulation and modeling have therefore emerged as indispensable tools to bridge quantum–mechanical descriptions with continuum-scale device behavior, offering predictive insight that is difficult to access experimentally. Early efforts focused on single-scale studies (e.g., density functional theory or simple porous-electrode models), but recent developments in high-performance computing, advanced numerical methods, and data-driven approaches now enable integrated modeling frameworks that explicitly link multiple scales in space and time.
In addition, the rapid expansion of nanomaterials libraries for electrodes, electrolytes, solid–electrolyte interphases (SEI/CEI), and current collectors has created a pressing need for computational screening and virtual prototyping. Multiscale methods are increasingly used to guide materials discovery, to interpret in situ/operando characterization, and to rationalize degradation mechanisms in realistic devices.
This Special Issue will highlight cutting-edge advances such as, but not limited to, the following: atomistic and electronic-structure modeling; mesoscale and microstructural modeling; continuum and device-level models; machine learning, data-driven, and reduced-order models; and Model–experiment integration.
Submissions that combine simulation, theory, and experiment, or that provide generalizable tools and insights applicable across different energy storage chemistries and nanomaterial platforms, are especially encouraged.
We welcome both original research articles reporting cutting-edge experimental or computational advances and comprehensive review papers.
Dr. Joonho Bae
Guest Editor
Manuscript Submission Information
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Keywords
- atomistic and electronic-structure modeling
- mesoscale and microstructural modeling
- continuum and device-level models
- machine learning, data-driven, and reduced-order models
- Model–experiment integration
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