Data-driven development of photocatalytic and optoelectronic perovskites

April 25th, 2023

R&D Focus Areas:
Photochemical and photocatalytic processes, Nanomaterials, Materials modelling

Lead Organisation:
RMIT University

Partners:
Shinshu University

Status:
Active

Start date:
April 2022

Completion date:
April 2025

Key contacts:
Professor Rachel A. Caruso – rachel.caruso@rmit.edu.au
Dr Tu Le – tu.le@rmit.edu.au

Funding:
AUD$375,000 – Australian Research Council (Discovery Project)

Project total cost:
AUD$830,000 – combined cash and in-kind contribution

Project summary description:
This project aims to use materials informatics to discover new, high efficiency perovskites for synthesis and testing in optoelectronic applications. This project expects to identify perovskite composition-property relationships to overcome current drawbacks of high-performance perovskites (contain rare or toxic elements and low stability in oxidative and humid environments) by considered selection of elements and their properties.

Expected outcomes from this project include new perovskites with commercial potential in critical areas such as energy conversion, photocatalysis and luminescence. This should provide significant benefits including approaches to materials discovery, novel materials and in renewable energy and environmental areas.

Project key objectives include:

  • Develop machine learning models for predicting optoelectronic properties (such as bandgap, decay time, quantum efficiency, photocurrent) of perovskites based on published experimental databases.
  • Apply machine learning models to rapidly screen perovskites of varied composition.
  • Synthesise, characterise and test the top performance perovskite materials (from the machine learning models) in photoluminescence, photovoltaics and photocatalytic applications.
  • Feed new experimental results into the models, improving prediction accuracy and optimising materials further.
  • Develop a fundamental understanding of feature-property relationships for improving perovskite performance.

Related publications and key links:
Mai, HX, Le, TC, Chen, DH, Winkler, DA, Caruso, RA Machine learning for electrocatalyst and photocatalyst design and discovery, Chemical Reviews 2022, 122, 13478-13515. https://pubs.acs.org/doi/10.1021/acs.chemrev.2c00061

Mai, HX, Le, TC, Chen, DH, Winkler, DA, Caruso, RA Machine learning in the development of adsorbents for clean energy application and greenhouse gas capture, Advanced Science 2022, 9, 2203899 (1-22).  https://onlinelibrary.wiley.com/doi/10.1002/advs.202203899

Mai, HX, Le, TC, Hisatomi, T, Chen, DH, Domen, K, Winkler, DA, Caruso, RA Use of metamodels for rapid discovery of narrow bandgap oxide perovskites iScience, 24, 103068 (1-19). Use of metamodels for rapid discovery of narrow bandgap oxide photocatalysts: iScience (cell.com)

Mai, HX, Li, X, Lu, J, Wen, X, Le, TC, Russo, SP, Chen, D, Caruso, RA Synthesis of layered lead-free perovskite nanocrystals with precise size and shape control and their photocatalytic activity J. Am. Chem. Soc. 2023, 145, 17337

Li, X, Mai, H, Lu, J, Wen, X, Le, TC, Russo, SP, Winkler, DA, Chen, D, Caruso, RA Rational atom substitution to obtain efficient, lead-free photocatalytic perovskites assisted by machine learning and DFT calculations Angew. Chem. Int. Ed. 2023, 62, e202315002 (1-12)

Higher degree studies supported:
Currently one PhD student at RMIT University.

 

Reviewed: August 2024