Mission & Approach

šŸŒ Our Mission

We advance the understanding of geochemical processes by integrating controlled laboratory experiments with predictive computational modelling.

Our goal is to help partners across exploration, extraction and environmental management to make informed decisions, reduce risk and improve sustainability.

šŸ”¬ Our Approach

We connect atomic-scale mechanisms to lab and field-scale outcomes through an integrated, reproducible workflow.

🧪 Experiment ↔ Model Integration

Hydrothermal and high-temperature/high-pressure (HTHP) experiments with in‑situ sampling are designed and interpreted alongside modelling, ensuring data and theory inform each other from day one.

āš›ļø Atomistic Mechanisms

Using DFT, AIMD, and MLMD, we quantify reaction energetics, surface complexes, and finite‑temperature behaviour at mineral–fluid interfaces to reveal how and why reactions proceed.

šŸ“ˆ Geochemical Prediction

We translate molecular insights into real-world operating windows through:

  • Reaction‑path modelling
  • Speciation and phase stability (e.g., Eh–pH, T–P diagrams)

šŸ—ŗļø Reactive transport (RTM)

Pore‑ to lab‑scale transport models predict element mobility andĀ reaction ratesĀ under realistic gradients, enabling scenario testing and sensitivity analysis.Ā 

🌈 Spectral validation

We computeĀ IR/Raman/optical/XASĀ features from atomistic models and overlay them with lab/synchrotron measurements (e.g., mASTRO Hydrothermal XAS cell) for robust assignment and quality control.Ā 

šŸ•‹ High Performance Computer (supercomputer) & reproducibility

Workflows run onĀ Pawsey Supercomputer Centre (Setonix),Ā National Computational Infrastructure (Gadi)Ā andĀ CSIRO HPC, orchestrated by ourĀ Molecular Modelling Toolbox (MMT)Ā with version control, provenance and shareable artefacts.Ā 

See also

Experimental Facilities → Hydrothermal reactors, mASTRO Hydrothermal XAS Cell, analytical labs

Computational capability → Method details and anchors for DFT, AIMD, MLMD, Thermodynamics & RTMĀ 

Case studiesĀ