Optimal Transport (OT) methods in Reduced Density Matrix Functional Theory (RDMFT)

The Ottawa-Queen's-Guelph-McMaster (OQGM) research network develop a novel multidisciplinary and integrated approach to address the problem of strong correlation in Chemistry and Material Sciences and aims to design new tools that are computationally efficient and easy enough for nonspecialists to use to guide their research and enrich their understanding.

Our research lies at the boundary between mathematics, physics, chemistry, machine-learning, and software design/deployment. Transcending boundaries between fields, in particular the physical, mathematical and computational aspects of chemistry and material sciences, is key for the development of scalable, rigorous and efficient algorithms.

This project receives funding from the New Frontiers in Research Fund (NFRF) - Exploration (Reference Number NFRFE-2021-00798).


Paul Ayers

McMaster, co-PI
Chemistry & Algorithms


Augusto Gerolin

uOttawa, PI
Mathematics & Chemistry


Farnaz Heidar-Zadeh

Queen's, PI
Chemistry & AI


Anna Kausamo

Florence (Italy), Collaborator


David Kribs

Guelph, co-PI
Mathematics of Quantum Information


Muho Nakata

RIKEN (Japan), Collaborator
Computational Chemistry & Numerical Linear Algebra


Kasia Pernal

Lödz (Poland), Collaborator
Chemistry & Quantum Physics

Emanuele Caputo

Postdoctoral Researcher


Dmitry Evdokimov

PhD student in Chemistry
AI & Chemistry


Nataliia Monina

PhD student
Math & Quantum Chemistry


Pavlo Pelikh

PhD student  
OT & Machine Learning


Zhiyi Lin

MSc student

We are looking for talented candidates to join our research team, including:

-- One PhD student in Mathematics: potential candidates are expected to have experience or interest in conducting research on Calculus of Variations, Optimization, Mathematical Physics, Numerical Linear Algebra and/or Optimal Transport.

-- One PhD student in Physics/Engineering: potential candidates must have a strong background in quantum mechanics and/or scientific computing. She/he should be motivated to learn state-of-art methods in computational chemistry and to develop deep learning methods for quantum chemistry.

-- One Postdoctoral Researcher in Computational Chemistry or AI: potential candidates are expected hold a PhD in Chemistry, Physics or Computer Sciences, have interest in developing AI methods for Chemistry, and a strong background in Computational Chemistry and/or Deep Learning.

Candidates are invited to apply by email via the address with subject “PhD Math OT-RDMFT 2023”, “PhD Phy OT-RDMFT 2023” or “Postdoc OT-RDMFT 2023”. The application package should include the following documents as attachment in a single pdf file:

1) Cover Letter;
2) CV with list of publications;
3) Postdoc position: Research statement on current and planned research (max 5 pages);
3') PhD position: Transcript of records and a brief description of research interest;
4) A list of at most three referees who are willing to write a letter of recommendation.

Selected candidates will be requested to have their referees send a letter of recommendation directly to the search committee, and will be invited for an online interview. Applications will be evaluated starting from November 15th. The position will remain open until the appropriate candidate is hired.

While in Canada, the young researchers will perform a highly collaborative and interdisciplinary research, co-supervised by the entire research team. The PhD students and postdocs are expected to travel during the year between Ottawa, Queen's, McMaster and Guelph to attend the events and work together with the PIs and co-PIs.


  • Fall 2022: Kick-off meeting

    18th-19th August, uOttawa

    Talks by Nataliia Monina, David Kribs, Augusto Gerolin and Farnaz Heidar-Zadeh.

    Dmitry Evdokimov joined our team as a PhD student and will work on computational aspects of Optimal Transport.

    Meeting picture


  • Summer 2023: Tutorials

    7th-12nd June
    Queen's University

    Talks by Valerii Chuiko, Fanch Coudreuse, Nataliia Monina, Dmitry Evdokimov and David Kribs.

    Valerii Chuiko and Nataliia Monina joined the team and will be working next year on computational aspects of Reduced Density Matrix Functional Theory (RDMFT).

    Pavlo Pelikh will also join the team and will be working on Machine Learning methods for RDMFT.

    Meeting picture

  • Summer 2024: Workshop

    TBA, McMaster University

  • 2024: Final meeting

    TBA, University of Guelph


Augusto Gerolin and Nataliia Monina. Non-commutative Optimal Transport for semi-definite positive matrices. Submitted (2023).