AI/ML-Assisted Computational Materials Design
Building data-aware molecular design workflows that use machine learning, structure-property relationships, and predictive descriptors to accelerate materials discovery.
Research
AI-guided optoelectronic materials discovery and excited-state engineering.
AURA Lab combines computational design, molecular simulations, photophysical insight, and device-oriented analysis to discover efficient organic emitters and semiconductor materials for next-generation optoelectronics.
Current programme
Six interconnected research themes
Quantum Chemical Calculations / Simulations for Optoelectronic Materials
Using quantum chemistry to model excited states, charge transfer, frontier orbitals, spin dynamics, and emissive pathways in candidate optoelectronic systems.
Data-Driven Discovery of Organic Emitter and Semiconductor Materials
Combining curated datasets, screening logic, and computational filtering to identify promising emitters and semiconductor platforms with improved performance windows.
Molecular Vibrations and Excited-State Engineering for Enhanced Radiative Decay
Controlling vibrational coupling, excited-state relaxation, and molecular rigidity to enhance radiative efficiency and suppress wasteful decay channels.
Triplet Management and Energy-Transfer Engineering in Optoelectronic Devices
Designing triplet-harvesting, host-guest, and donor-acceptor frameworks that improve energy transfer, exciton utilization, and operational stability.
Hyperphosphorescence and Next-Generation OLED Technologies
Developing material strategies for hyperphosphorescent OLEDs and advanced emissive architectures with high efficiency, strong colour purity, and low roll-off.
Methods & capabilities
A computation-to-device workflow
- AI/ML-assisted materials screening and design-rule extraction
- Quantum-chemical calculations for excited-state and electronic-structure analysis
- Data-driven prioritization of emitters and semiconductor candidates
- Molecular photophysics, radiative decay analysis, and triplet-state interpretation
- Steady-state and time-resolved spectroscopy
- Electrochemistry and structure-property correlation
- OLED-oriented materials assessment and device-relevant interpretation
- Energy-transfer analysis for advanced emissive architectures
Application pathways
Where these themes create impact
- High-efficiency OLED emitters and host systems
- Hyperphosphorescent device architectures
- Organic semiconductor discovery and optimization
- Excited-state control for colour purity and radiative efficiency
- Triplet harvesting and energy-transfer optimisation
- Predictive design frameworks for next-generation optoelectronics
Featured direction
Hyperphosphorescence and computationally guided OLED design.
A major direction of the group is the rational design of emissive systems for advanced OLED technologies. By coupling computational screening with excited-state engineering and triplet management strategies, we aim to identify material combinations that improve external quantum efficiency, reduce roll-off, and enable more robust next-generation display and lighting platforms.
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