‘Science asks you to be comfortable with uncertainty, which is easier to say than to live with day to day.’

Dr Sousa Javannikkhah is leading a new research group at Maynooth University focusing on molecular multiscale modelling.

Javannikkhah’s background is in chemical engineering, with a PhD focused on computational modelling of polymeric composites using molecular dynamics simulations.

She developed multiscale computational methods for soft matter and self-assembling polymer systems during her postdoctoral positions, after which she went on to hold two consecutive Marie Skłodowska-Curie fellowships at the University of Limerick, applying computational chemical engineering to design drug delivery platforms for monoclonal antibodies and anticancer drugs.

Currently, Javannikkhah works as an assistant professor in computational chemistry at Maynooth University. She was awarded a Research Ireland Pathways grant earlier this year to kickstart her research group Simulation of Structures Across Scales group (SUSAS) as its principal investigator.

What inspired you to become a researcher?

I cannot deny the effect of teaching on my research, digging into fundamental theories, mathematics, and connecting these to practical, real-world applications.

I combined my PhD studies with temporary lecturer positions and taught various modules in the fields of chemical and polymer engineering. I loved (and still do!) the energy and interactions with the students.

I was lucky enough to work with many inspiring mentors and geniuses in their own way, particularly my PhD supervisor, Professor Moghbeli, and my postdoc mentor, Professor Vandichel, with whom I had the pleasure of collaborating for over 4 years while part of their research groups.

Their smartness, holistic way of thinking, support, and belief in me have made an everlasting impact, allowing me to keep feeling that research spark and, above all, belief in myself.

Can you tell us about the research you’re currently working on?

My research sits at the interface of computational chemistry and chemical engineering, using simulation to design and understand complex materials before they are ever made in the lab.

My group, the SUSAS group at Maynooth, works across several interconnected areas: designing polymeric drug delivery systems for cancer and biologics, developing membrane materials for hydrogen fuel cells, modelling composites and adhesion at the molecular level, and studying porous materials for gas capture and separation.

What connects all of these is the same core question; How do molecular-level decisions determine the properties we actually care about at the scale of a device, a formulation, or a patient?

The work has evolved significantly over time, from purely atomistic simulation, through mesoscale methods like dissipative particle dynamics, to now integrating machine learning into our workflows to accelerate discovery.

That broadening of scale – from nanometres to metres, from nanoseconds to seconds – mirrors the translational ambition behind the work: the idea that a simulation can ultimately guide the design of a medicine that helps a real patient, or a membrane that makes clean energy viable, is what continues to drive me. We work closely with experimental and industry partners, which keeps the research grounded in real problems.

In your opinion, why is your research important?

Every medicine we swallow, every membrane filtering our water, every composite holding a turbine blade together, all of it begins with decisions made at the molecular scale, mostly invisible, rarely celebrated. Yet designing these materials through trial and error in the lab is slow and expensive.

Computational modelling allows us to explore enormous design spaces rapidly, identify promising candidates, and understand the mechanisms that govern material behaviour, before a single experiment is run. Our work helps bridge the gap between molecular insight and real-world application.

In drug delivery, this can mean the difference between a therapy reaching patients or failing in development. In energy, it can accelerate the design of membranes for hydrogen fuel cells. In sustainability, it can guide the development of materials that capture carbon or filter pollutants. The common thread is this; We can now design matter with intention, not just intuition. That shift, from trial and error to molecular understanding, is what my research is about.

What commercial applications do you foresee for your research?

There are several exciting avenues. In drug delivery, our computational platforms can accelerate the design of oral formulations for biologics, a market with enormous unmet clinical need.

We have already filed two invention disclosures with the University of Limerick’s Technology Transfer Office and are preparing a patent application for a novel polymeric drug delivery platform.

In the area of porous materials, our simulation tools have direct applications in carbon capture, gas storage and separation, and the design of components for hydrogen fuel cells and energy storage devices.

More broadly, our AI/ML-enabled modelling approaches could be licensed or spun out as digital tools for pharmaceutical and materials companies seeking to reduce experimental costs and accelerate discovery.

What are some of the biggest challenges you face as a researcher in your field?

One of the deepest scientific challenges in my field is bridging scales – the phenomena we care about, how a drug is captured inside a polymer carrier, how a membrane lets ions through, how a composite responds to stress, happen at the molecular level, but their consequences play out at scales we can actually measure and use. No single simulation method spans that gap, and we do not always get it right first time.

On a more personal level, one of the challenges I did not fully anticipate when I became an independent researcher is how much of the job is about sustaining other people’s confidence as well as your own.

When a simulation gives unexpected results, a funding application is rejected, or a project stalls, you have to find a way to keep going and keep your team motivated.

Science asks you to be comfortable with uncertainty, which is easier to say than to live with day to day. I have learned to see that uncertainty not as failure, but as the space where discovery actually happens.

Are there any common misconceptions about this area of research?

A common misconception is that computational research is purely theoretical and disconnected from real applications. In fact, the work my group does is deeply translational.

We work hand-in-hand with experimentalists and industry partners, and our simulations are directly validated against experimental data.

Computation is not a replacement for experiment; it is a powerful complement that can focus experimental effort and generate hypotheses that would be impossible to arrive at by intuition alone.

Another misconception is that AI/ML-based simulations will simply solve materials design, that you feed in data and the answers come out.

In reality, building reliable models requires deep chemical and physical intuition, carefully curated data, and rigorous validation against experiment. A model trained on poor assumptions will give you the wrong answer confidently and at speed. AI is a powerful tool, but it still needs a scientist holding it. It is science, not magic.

What are some of the areas of research you’d like to see tackled in the years ahead?

I am particularly excited about three interconnected directions.

First, the design of next-generation polymeric drug delivery systems. I would love to see simulation-guided platforms that can deliver biologics and anticancer agents orally with high efficiency and patient-friendly formulations, reducing the need for injections and improving quality of life.

Second, I am deeply interested in the application of machine-learning-constructed interatomic potentials and hybrid simulations to flexible porous materials such as metal–organic frameworks. These materials show extraordinary promise for carbon capture, gas storage, and sensing, and I believe ML-enabled simulation will unlock their full potential.

Third, I am excited about the role of computational design in developing membrane materials for fuel cells and clean energy applications, work that my group is now pursuing through a Research Ireland Pathways Programme grant.

Across all three areas, I see a future where computation, AI, and experiment work seamlessly together to accelerate discovery and translation.

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