Key Takeaways & Executive Findings
- •• • MD simulations provide atomic-level insights into adsorption mechanisms, enabling rational design of adsorbents with enhanced capacity and selectivity, as demonstrated in studies of hierarchical porous carbon for antibiotic removal (Xu et al., 2025). • • In bioremediation, MD combined with molecular docking identifies key enzyme-substrate interactions, as shown for laccase-mediated degradation of industrial dyes (Pande et al., 2022), facilitating engineering of robust biocatalysts. • • MD simulations reveal competitive adsorption and transport phenomena, such as CO2/H2O in graphene nano-slits (Fan et al., 2025), guiding membrane design for efficient gas separation. • • Integration of MD with AI and machine learning is poised to overcome current computational bottlenecks, enabling high-throughput screening and accurate prediction of pollutant-material interactions, thus accelerating the development of next-generation remediation technologies.
Abstract
Environmental pollution severely impacts ecosystems, human health, and socio-economic development, necessitating efficient removal and detoxification of pollutants. Traditional trial-and-error approaches are inadequate for developing high-performance environmental materials and remediation technologies. Molecular dynamics (MD) simulations have emerged as essential tools for elucidating pollutant removal and toxicity mechanisms at the atomic-molecular level. This review summarizes core computational methods of MD simulations, including force fields, ensemble settings, and enhanced sampling techniques. It then discusses applications in novel adsorbent materials, bioremediation (enzyme catalysis), membrane separation, and membrane fouling, highlighting how MD reveals microscopic interaction mechanisms. Current limitations, such as force field accuracy, timescale constraints, and system size, are critically assessed. Future integration with artificial intelligence (AI) and machine learning is explored for accelerating simulations, improving force field parameterization, and enabling high-throughput screening. The review aims to promote mechanism-based design and diversified development of environmental pollution control materials and remediation technologies.
1. Introduction
Environmental pollution from heavy metals, persistent organic pollutants, and emerging contaminants poses severe threats to ecosystems and human health. Conventional remediation strategies, such as adsorption, advanced oxidation, and biodegradation, often rely on empirical optimization, leading to suboptimal performance and high costs. The lack of mechanistic understanding at the molecular level hinders the rational design of efficient and selective materials and biocatalysts, creating a critical bottleneck in environmental technology development.
Molecular dynamics (MD) simulations offer a powerful solution by providing dynamic, atomic-scale insights into pollutant-material interactions. This review systematically examines the current state and challenges of MD simulations in environmental remediation, covering novel adsorbents, enzymatic degradation, and membrane processes. By integrating MD with artificial intelligence, we can overcome existing limitations and accelerate the transition from trial-and-error to mechanism-based design, ultimately enabling the development of high-performance, cost-effective remediation technologies.
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WAN Jia, ZHU Shiye, CHEN Anwei (2026). Molecular Dynamics Simulation in the Mechanism Exploration Research of Environmental Remediation: Current Status and Challenges. Environmental Chemistry. https://doi.org/10.7524/j.issn.0254-6108.2026010502
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Frequently Asked Questions
What are the primary limitations of current MD simulations in accurately predicting pollutant adsorption on heterogeneous environmental surfaces?
Current MD simulations often rely on simplified force fields that may not accurately capture polarizability, charge transfer, or reactive bond breaking/formation. Additionally, the timescales accessible (typically nanoseconds to microseconds) are insufficient to observe slow diffusion or conformational changes in complex environmental matrices. System sizes are also limited, preventing simulation of realistic porous materials or biofilms. These limitations can lead to quantitative inaccuracies in binding energies and adsorption isotherms, necessitating validation with experimental data.
How can MD simulations be integrated with experimental techniques to validate mechanistic hypotheses in enzyme-mediated bioremediation?
MD simulations can predict enzyme-substrate binding poses and identify key residues involved in catalysis. These predictions can be validated by site-directed mutagenesis experiments, where specific residues are altered and the resulting changes in degradation kinetics are measured. Additionally, MD-derived binding free energies can be compared with experimental kinetic parameters (e.g., kcat/Km) to confirm the rate-limiting steps. This integrated approach provides a robust framework for understanding and engineering enzymes for enhanced pollutant degradation.
What are the computational costs and scalability challenges of applying MD simulations to large-scale environmental systems, and how can AI mitigate these?
MD simulations of large systems (e.g., >1 million atoms) require significant computational resources, often limiting simulation timescales. AI techniques, such as machine-learned force fields, can accelerate simulations by orders of magnitude while maintaining accuracy. Additionally, AI can be used to enhance sampling methods (e.g., via reinforcement learning) to explore rare events, and to perform high-throughput screening of material libraries, thus enabling the study of complex environmental processes that are currently intractable.
How do MD simulations account for the effects of environmental conditions (pH, ionic strength, temperature) on pollutant-material interactions?
MD simulations can incorporate explicit solvent models with ions and adjust protonation states to mimic pH conditions. However, accurately modeling pH effects requires constant-pH MD methods, which are computationally demanding. Ionic strength is typically modeled by adding explicit ions, but their concentrations must be carefully controlled to avoid artifacts. Temperature is controlled via thermostats. While these approaches provide qualitative trends, quantitative predictions under varying environmental conditions remain challenging due to force field limitations and sampling issues.
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