Recent Advance in Molecular Modeling and Simulation of Nanomaterials
A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Theory and Simulation of Nanostructures".
Deadline for manuscript submissions: 26 January 2026 | Viewed by 82
Special Issue Editors
Interests: molecular simulation calculation and machine learning; design, modeling and separation mechanism of gas separation membranes; wastewater treatment
Interests: design and synthesis of porous organic framework membranes; metal–organic framework membranes and mixed matrix membranes, as well as their application research in gas separation and water treatment
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The urgent demand for efficient separation materials—critical to energy sustainability, environmental remediation, and resource recovery—drives the need for rational nanomaterial design beyond traditional experimental approaches. This Special Issue highlights the transformative methodologies accelerating this pursuit. We showcase detailed computational studies of diverse nanostructures, where atomistic simulations decode structure-property relationships, stability, and interfacial behavior. Concurrently, we emphasize molecular simulation of adsorption and membrane materials, enabling precise predictions of essential separation performance metrics—selectivity, adsorption capacity, diffusion kinetics, and permeance—for applications like CO2 capture, hydrogen purification, and water desalination. Critically, this issue explores the integration of machine learning (ML) techniques in material design, where deep learning, generative models, and high-throughput screening synergistically optimize nanostructures, predict emergent properties, and guide synthesis protocols, transcending conventional computational limits. By bridging fundamental nanoscale insights with performance-driven simulations and data-driven innovation, this collection demonstrates how computational strategies can overcome experimental bottlenecks, unlock novel material paradigms, and deliver scalable, sustainable solutions to pressing global challenges. We invite original research, reviews, and perspectives advancing these frontiers.
Dr. Dongyang Li
Dr. Meixia Shan
Guest Editors
Manuscript Submission Information
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Keywords
- molecular modeling and simulation
- machine learning
- artificial intelligence
- computational material science
- nanostructures
- adsorption
- membrane separation
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