Molecular Dynamics Simulations: Applications and Advances
A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 6
Special Issue Editors
Interests: quantum chemistry and solid state; molecular dynamics; Monte Carlo; multiscale simulation; HPC and parallel algorithms
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Molecular dynamics (MD) simulations have become a powerful and widely used approach for exploring the behavior of matter at the atomic and molecular level. By modeling the interactions and time evolution of atoms and molecules, MD enables the study of complex systems and dynamic processes that are often difficult or inaccessible to experimental observation. These simulations provide key insights into the structural, kinetic, and thermodynamic properties of systems across chemistry, biology, and materials science.
With continuing advances in computational power, the progress and design of algorithms, and theoretical frameworks, MD simulations are playing an increasingly important role in both fundamental research and applied sciences. In particular, developments in enhanced sampling techniques, machine learning integration, force field optimization, and ab initio approaches are pushing the boundaries of what MD can achieve.
This Special Issue of Molecules, titled "Molecular Dynamics Simulations: Applications and Advances", aims to present recent progress in both methodological developments and practical applications of MD. We welcome original research articles, reviews, and case studies covering algorithmic innovations, interdisciplinary applications, and simulations that provide new insights into molecular-level phenomena.
Dr. Viorel Chihaia
Prof. Dr. Godehard Sutmann
Guest Editors
Manuscript Submission Information
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Keywords
- force fields and interaction models
- ab initio molecular dynamics for ground and excited states: adiabatic and non-adiabatic approaches
- adaptive potentials and hybrid interactions
- enhanced sampling techniques
- free energy calculations and related methods
- hybrid and multiscale modeling
- integrators and thermostat/barostat algorithms
- machine learning and AI in molecular dynamics
- applications of MD in life sciences, materials, and energy
- system preparation and velocity assignment
- trajectory, structural, kinetic, and dynamic analysis
- hardware acceleration, parallelization, and software development
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