Machine Learning-Assisted Design of Membranes for Wastewater Treatment, Desalination and Gas-Separation

Faculty Development Competitive Research Grants Program 2025-2027

The proposed research aims to tackle critical challenges in natural resource management and efficient energy use by integrating Machine Learning (ML) and data-driven technologies in the relevant processes. Our primary focus will be the artificial-intelligence-assisted (AI-assisted) design of advanced materials and structures which will enable energy-efficient applications for wastewater treatment, desalination and hazardous gas separation to be developed. This aim will be accomplished be leveraging the synergistic capabilities of molecular dynamics (MD) simulations and AI techniques in the design of graphene-based membrane structures targeting the applications above. Through extensive simulations, we will gain atomic-level insights into the properties of relevant graphene-based structures, while AI algorithms will efficiently analyze the vast datasets generated, revealing relevant patterns and correlations. Therefore, by addressing the challenges pertaining to water treatment, hazardous gases management, and efficient energy use in the respective processes, our research endeavors to significantly contribute to mitigating measures against anthropogenic environmental pollution and climate change. Our research is especially relevant to Kazakhstan, a country facing pressing environmental issues related to freshwater scarcity, water and air pollution with Astana and Almaty being heavily polluted and rapidly developing cities. By designing high-performance graphene-based hazardous gas separation applications, we aim to alleviate pollution concerns, and contribute to the development of methods that improve air quality and consequently public health in the region. At the same time, our proposed research on waste-water treatment and water desalination brings the promise of innovative solutions to address water quality and scarcity issues in various regions of Kazakhstan, benefiting local communities and ensuring sustainable water management practices.
The potential benefits of our research span multiple disciplines, including materials science, nanotechnology, computational modeling and relevant engineering fields. By providing new insights into the behavior of graphene-based materials and their interactions, our work advances the fields of molecular dynamics simulation and machine learning, fostering innovations in efficient design of membranes and separation processes. By delving into the molecular level, we aim to deepen our understanding of these materials, paving the way for more informed, efficient, and innovative designs. In summary, our scientific proposal envisions harnessing the potential coming from the coupling of molecular dynamics simulations and machine learning techniques to optimize graphene-based membranes for waste-water treatment, hazardous gas separation, and water desalination. The direct relevance of our work to Kazakhstan's environmental challenges underscores its potential to drive positive impacts for the country and its communities.