Saturn’s moon Titan is the only other celestial body in our solar system with a thick atmosphere and liquid lakes on its surface, except the rain there is liquid methane, the temperature sits near 90 K, and the chemistry is rich enough that it has been referred to as a ‘prebiotic laboratory on a planetary scale’. As sunlight breaks apart the nitrogen and methane in Titan’s air, a steady drizzle of organic molecules builds up, settling into dunes, sediments and exotic minerals of which we have limited understanding.
To make sense of what is actually on the surface and to interpret data from missions like NASA’s Dragonfly, due to reach Titan in 2034 we need to predict which crystals these simple organic molecules form, how they grow, and what properties they have. The same problem – polymorphism – that decides whether a new medicine dissolves properly or a new material performs adequately.
This project develops and applies computational methods to answer those questions. Working across computational chemistry, materials science and high-performance computing, the student will model intermolecular interactions, search for likely crystal structures, simulate how crystals grow from Titan’s vapour and hydrocarbon liquids (and from solution here on Earth), and calculate the properties that follow. Machine learning techniques can be directly incorporated throughout, or where they sharpens speed or accuracy.
There is genuine room to shape the project toward your strengths and background designing interaction models, building crystal structure prediction workflows, simulating growth, or computing mechanical and thermal properties. Throughout, predictions will be tested against real measurements with international experimental collaborators, and the methods are to be released as open-source software.
Aim
The project aims to improve our ability to predict the structure, growth and properties of organic molecular crystals under conditions ranging from Earth’s laboratories to Titan’s frozen surface. It seeks to build faster and more accurate models of the interactions between molecules, use these to predict and rank likely crystal structures, simulate how those crystals grow in realistic environments, and connect these atomic-scale predictions to the bulk properties that matter for both planetary science and industry.
Objectives
Depending on the student’s interests and background, the project will pursue a selection of the following:
- Develop and test computational models of intermolecular interactions that are accurate yet cheap enough to screen many candidate structures suited to the weakly bound hydrocarbons and nitriles found on Titan.
- Apply crystal structure prediction to find the most likely packing arrangements for chosen molecules, including multi-component systems such as co-crystals.
- Simulate crystal growth from vapour and from liquid to predict crystal shapes, growth rates and which forms appear under Titan-like (and Earth-like) conditions.
- Calculate key physical properties e.g. elastic, mechanical and thermal behaviour and link them to crystal shape and bulk material performance.
- Validate predictions against experimental data through established collaborations and contribute the methods to open-source software.
Significance
For Titan, these methods will help interpret what future missions actually find on the surface and what it tells us about the moon’s geology and its potential for prebiotic chemistry. The very same chemistry governs molecular crystals on Earth: the crystal form controls how a drug is manufactured and how well it works in the body and underpins advances in agrochemicals, energetic materials and flexible electronics.
Because the methods are deliberately general, success reaches well beyond any single use, and making the tools openly available lets researchers everywhere build on them. For the student, this is training in a genuinely multidisciplinary, internationally connected field combining the appeal of planetary science with skills in strong demand across academia and industry.
Ideal Candidate
We are looking for a self-motivated PhD candidate with excellent organisation, problem-solving and communication skills, and a genuine curiosity for working at the boundary of chemistry, physics, materials science and computing. Candidates with strong quantitative skills and a background in chemistry, physics, computer science or a related discipline are encouraged to apply. Familiarity with scientific programming (for example Python), computational chemistry or molecular modelling, and working in a Linux or high-performance computing environment is desirable but not essential: a willingness and curiosity to learn matters most. Additionally, the applicants should meet the eligibility criteria for entry into a PhD program at Curtin University.
This project is open to International and Domestic applicants.
Scholarship
If you are identified as the preferred candidate for this project, you may be considered for an RTP scholarship.
Enquires and How to Apply
For enquires about this opportunity contact Dr Peter Spackman at Peter.Spackman@curtin.edu.au
To formally apply submit an Expression of Interest to Dr Peter Spackman during the Central Scholarship round (July 1st – July 31st 2026)