‘Science asks you to be comfortable with uncertainty, which is easier said than done every day.’
Dr Sousa Javannikkhah leads a new research group at Maynooth University focusing on multiscale molecular modeling.
Javannikkhah’s background is in chemical engineering, with a PhD focusing on structural modeling of polymeric compounds using molecular dynamics simulations.
He developed a number of methods for synthesizing soft materials and self-assembled polymer systems during his postdoctoral position, after which he went on to hold a Marie Skłodowska-Curie fellowship at the University of Limerick, using synthetic chemical engineering to design drug delivery platforms for monoclonal antibodies and anti-cancer drugs.
Currently, Javannikkhah works as an assistant professor in computational chemistry at Maynooth University. He was awarded a Research Ireland Pathways grant earlier this year to start his own research group the Simulation of Structures Across Scales group (SUSAS) as its principal investigator.
What inspired you to become a researcher?
I can’t deny the impact of teaching on my research, digging into basic theories, math, and connecting these to practical, real-world applications.
I combined my PhD studies with part-time teaching positions and taught various modules in the fields of chemical and polymer engineering. I loved (and still do!) the energy and connection with the students.
I was fortunate to work with many mentors who were inspiring and brilliant in their own way, especially my PhD supervisor, Professor Moghbeli, and my postdoc advisor, Professor Vandichel, with whom I had the pleasure of working for over 4 years while I was part of their research groups.
Their wisdom, holistic thinking, support, and belief in me have had a lasting impact, allowing me to always feel that spark of research and, above all, belief in myself.
Can you tell us about the research you are currently working on?
My research lies in the interface of computational chemistry and chemical engineering, using simulation to design and understand complex materials before they are made in the lab.
My group, the SUSAS group in Maynooth, works in several interconnected areas: designing polymeric delivery systems for cancer drugs and biologics, building membrane materials for hydrogen fuel cells, modeling compounds and adhesion at the molecular level, and studying porous materials for gas capture and separation.
What connects all of these are the same core questions; How do molecular determinants determine the features we really care about at the scale of a machine, structure, or patient?
The work has evolved over time, from pure atomistic simulations, through mesoscale methods such as dissipative particle dynamics, now incorporating machine learning into our operations to accelerate discovery.
That expansion of the scale – from nanometers to meters, from nanoseconds to seconds – shows the desire for translation behind the work: the idea that simulation can eventually guide the design of a drug that helps a real patient, or a membrane that enables clean energy to work, is what continues to drive me. We work closely with research and industry partners, which keeps research focused on real problems.
In your opinion, why is your research important?
Every drug we swallow, every membrane that filters our water, every compound that holds a turbine blade together, all begin with decisions made at the molecular level, often invisible, rarely celebrated. However designing these things by trial and error in the lab is slow and expensive.
Computational modeling allows us to quickly evaluate large design gaps, identify promising candidates, and understand the mechanisms that govern important behaviors, before a single experiment is conducted. Our work helps bridge the gap between molecular understanding and real-world application.
In drug delivery, this can mean the difference between a treatment reaching patients or failing to grow. With energy, it can speed up the membrane formation of hydrogen fuel cells. In sustainability, it can guide the development of materials that capture carbon or filter pollutants. The common thread is; Now we can design matter with intention, not just by intuition. That transition, 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 interesting methods. In drug delivery, our computing platforms can accelerate the design of oral biologics, a market with significant 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 specific applications in carbon capture, gas storage and separation, and the design of components for hydrogen fuel cells and energy storage devices.
Broadly, our AI/ML-enabled modeling methods can be licensed or deployed as digital tools for pharmaceutical and manufacturing companies looking to reduce testing costs and accelerate discovery.
What are some of the biggest challenges you face as a researcher in your field?
One of the most profound scientific challenges in my field is the aggregation scales – the phenomena we care about, how the drug is taken inside the polymer carrier, how the membrane allows ions to pass, how the compound reacts to stress, it happens at the molecular level, but its effects play out on a scale that we can measure and use. There is no single acting method that closes that gap, and we don’t always get it right the first time.
On a personal level, one of the challenges I didn’t fully anticipate when I became an independent researcher was how much work is involved in maintaining other people’s confidence as well as your own.
If the simulation gives unexpected results, a funding request is rejected, or the project is stuck, you have to find a way to move forward and keep your team motivated.
Science asks you to be comfortable with uncertainty, which is easier said than done every day. I’ve learned to see that uncertainty not as failure, but as a place where discovery actually happens.
Are there any common misconceptions about this area of research?
A common misconception is that computer science is only theoretical and not connected to real applications. In fact, the work my team is doing translates deeply.
We work closely with testers and industry partners, and our simulations are directly validated against test data.
Counting is not a substitute for testing; it is a powerful complement that can focus the experimental effort and generate ideas that cannot be reached by intuition alone.
Another misconception is that AI/ML-based simulations will simply solve the structure of objects, which you will feed them with data and the answers will come out.
In fact, building reliable models requires deep chemical and physical intuition, carefully selected data, and rigorous validation against experiments. A model trained in negative thinking will give you negative feedback with confidence and speed. AI is a powerful tool, but it still needs a scientist to master it. Science, not magic.
What are some of the research areas you would like to see addressed in the coming years?
I am very happy with the three directions that are connected.
First, the design of next-generation polymeric drug delivery systems. I would like to see simulation-guided platforms that can deliver biologics and anti-cancer agents orally in a more efficient and patient-friendly way, reducing the need for injections and improving quality of life.
Second, I am very interested in the application of mechanical interatomic capabilities and hybrid simulations to soft and porous materials such as metal-organic frameworks. These materials show incredible promise for carbon capture, gas storage, and sensors, and I believe ML-enabled simulations will unlock their full potential.
Thirdly, 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 currently undertaking with a grant from the Research Ireland Pathways Programme.
In all three areas, I see a future where computing, AI, and testing work seamlessly together to accelerate discovery and translation.
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