Molecular Simulations and Data-driven Modelling for Polymer Nanocomposite Membranes

The urgent need to mitigate climate change has led to increased focus on sustainable energy solutions. This includes the development of mixed matrix membranes (MMMs) for gas separation in net-zero applications. However, optimizing MMMs for efficient gas separation remains a challenge, due to the many factors that influence the membrane performance, such as size, shape, surface chemistry, and loading of the fillers. In addition, the area currently lacks systematic modelling approaches and comprehensive structure-property correlations, hindering the development of a more efficient design process. Molecular modelling can be applied to gain understanding on the microscopic interactions between polymer matrices and dispersed fillers and elucidate gas transport mechanisms, to identify features for enhanced selectivity and permeability for various separation applications.

Moreover, a substantial body of experimental evidence exists in literature, presenting an ideal opportunity for leveraging data-driven methods. The application of Machine Learning (ML) to estimate the properties of polymer membranes for gas separation, including solubility, diffusivity, and permeability is gaining traction. However, significant untapped potential exists in their application to the design of Mixed Matrix Membranes for gas separation.

The goal of this project is to apply molecular simulations and machine learning techniques to design MMMs with superior gas separation performance. This combined modelling approach can play an important role in expediting the transition from laboratory testing to market implementation, contributing to the transition towards a net-zero carbon footprint.

The scientific objectives of the project can be articulated as follows:

  1. Employ molecular dynamics simulations to investigate the interaction between polymer matrices and filler materials, to elucidate the impact on membrane properties and gas transport performance and create new molecular descriptors for ML modelling.
  2. Develop ML models to predict gas solubility, diffusivity, and permeability of MMMs based on polymer molecular structure and filler properties.
  3. Utilize molecular simulation results and ML predictions to guide the design and optimization of MMM formulations for enhanced gas separation efficiency.

Educational and research opportunities for the successful applicant:

  • training in state-of-the-art molecular simulation and machine learning techniques;
  • access to a range of computational facilities, including ARCHER2, the UK’s national supercomputer; 
  • gaining expertise in polymer nanocomposite materials and membrane separation processes;
  • close interaction with an interdisciplinary network of researchers at the Institute for Materials and Processes (IMP) focused on the study of polymer membranes and MMMs
  • improvement of public speaking skills, through participation to national and international scientific conferences for the dissemination of research findings;
  • development of academic writing skills, through publication of research outcomes in leading scientific journals.

Further Information: 

The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here:

Closing Date: 

Sunday, May 26, 2024

Principal Supervisor: 

Assistant Supervisor: 



Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.


Tuition fees + stipend are available for Home/EU and International students

Further information and other funding options.

Informal Enquiries: 

Dr Eleonora Ricci,