PhD Computer-aided Collector Design of Peptide Materials for Rare Earth Element Recovery

This project aims to improve rare earth element recovery by designing peptide-polymer composites inspired by lanthanide-binding proteins using machine learning and computational chemistry.

Rare earth elements are critical for modern electronics, renewable energy, and storage technologies, but their recovery poses significant environmental challenges due to complex separation and purification processes.

This project aims to enhance the extraction and separation of rare earth elements (REEs) by developing innovative peptide-polymer composite materials inspired by bio-mimicry using machine learning and computational chemistry. The project draws inspiration from selective lanthanide-binding proteins like Lanmodulin (LanM), which exhibit exceptional affinity and selectivity for lanthanides.

LanM’s unique structure, influenced by specific residues, allows it to bind REEs effectively. However, current approaches that incorporate the entire LanM protein into elastin backbones are inefficient, as only a small fraction actively binds lanthanides, and competing properties reduce effectiveness. To overcome these limitations, this project will use computer-aided design to develop pnew peptide sequences for improved REE selectivity.

As part of a wider collaboration in the CoEMinerals involving molecular modelling, peptide synthesis and polymer chemistry, this interdisciplinary project ultimately aims to develop new stimuli-responsive peptide-polymer composite materials for efficient and environmentally sustainable REE recovery.

Funding: $37,400 per annum (2025 rate) indexed annually plus a $10,000 per annum scholarship top-up. For a PhD candidate, the living allowance scholarship and tuition fee scholarship are for 3.5 years. Scholarships also include up to $1,500 relocation allowance and Overseas Student Health Cover at single rate, for an international candidate.

Supervisor: Professor Alister Page

Available to: Domestic and International students

PhD

• The successful applicant will have a Masters or Honours level degree in chemistry or a related discipline.
• Demonstrated experience in machine learning and molecular dynamics is desirable.
• The applicant will need to meet the minimum eligibility criteria  for admission.

Interested applicants should send an email expressing their interest along with scanned copies of their academic transcripts, CV, a brief statement of their research interests and a proposal that specifically links them to the research project.

Please send the email expressing interest to Alister.Page@newcastle.edu.au by 5pm on 31 December 2025.