Research
Our Research Tools
In nexSAS, we have developed a platform - AiNU - for extraction of fundamental materials parameters using physics-based machine learning. It has been used in a study of thermal degradation of perovskite, and it serves as one of the cornerstones of the studies in nexSAS.
We anticipate intensive use of AiNU in future across a diverse array of projects and we are excited about collaboration. If you think AiNU or any of our works might be useful for your research, please do not hesitate contacting us at [email protected].
Research Projects
Embodied AI for accelerated discovery of next-generation photovoltaic materials
This research is based on a collaborative ACAP2 project among Monash, ANU, and UNSW entitled Bayesian Optimization and Large Language Modules: Towards Autonomous, Closed-Loop PV Materials Discovery.
Atomic origin of instability at the perovskite interfaces
AI-enhanced computational study of interface physics.
Development of a low-cost and quantitative materials analysis platform by physics-based machine learning
ACAP Postdoctoral Fellowship (R4, 2023).
Transport phenomenon at the tri-phase interface in hydrogen energy systems
This research investigates the transport of electrons, ions, and molecules at the tri-phase interface in hydrogen energy systems.
Development of user friendly machine learning toolkit for perovskite solar cell analysis
An Honours project for Bachelor of Computer Science