Alfred Deakin Postdoctoral Research Fellow

PhD Scholarship in Developing Solid Electrolytes using Machine Learning

Call for Applications – PhD in Developing Solid Electrolytes

A PhD scholarship is available at the Institute for Frontier Materials (IFM), Deakin University. This project will develop solid electrolytes using combined machine learning (ML), molecular modelling, and experimental characterisation methods. We are looking for applications from enthusiastic candidates.

[PhD project description]

Project title: Machine Learning-assisted Development of Highly Functional Organic Ionic Plastic Crystals for Solid-state Batteries

Background: Safe, long-lasting, and high-performance rechargeable batteries are required to meet the ever-growing demand for battery-driven worldwide electrification. Due to the safety issue of current lithium-ion batteries (LIBs), this project will aim at developing solid electrolytes that could help address the safety concerns in the next generation of batteries. Here a novel class of solid electrolytes, named organic ionic plastic crystals (OIPCs) will be explored using combined ML, molecular simulations, and experimental characterisation approaches. The outcome will contribute to the new OIPC development, thereby increasing opportunities to use OIPCs for solid-state battery (SSB) applications. It is also expected to generate novel knowledge to fill gaps in this frontier research area, which may lead to breakthrough results.

Project details: The project has three stages: (1) Dataset collection which involves both experimental and simulation data, (2) Training of ML models and making predictions and (3) Experimental validation and further computational investigation. Special attention is paid to the development of OIPCs, which are emerging soft solid electrolytes for SSBs, but they have yet to be studied strategically using ML. In Stage 1, the research data will be collected from the previous publications (>50 papers) describing the physicochemical properties of OIPCs. For some results not reported but could be obtained computationally, additional computation will be conducted to fill gaps. In Stage 2, using this dataset, the correlation among several properties, such as OIPC crystalline phase structures, ionic conductivity, electrochemical potential window, thermal behaviour, and ionic interactions will be analysed via ML methods. The trained model then will be used to predict the properties of a few pre-known systems for validation. Then the validated ML methods will be used to predict new OIPCs with potentially promising properties. In Stage 3, the synthetic routes of selected new OIPCs will be designed and prepared with the help of the synthesis team. The predicted properties of these OIPCs will be confirmed experimentally. Furthermore, the most promising OIPC will be investigated computationally to give further in-depth understanding at an atomistic level.

The project will be supervised by the IFM’s leading researchers in the battery field, Dr Hiroyuki Ueda, Dr Fangfang Chen, Prof. Jenny Pringle, and Prof. Patrick Howlett. The project will start in early- or mid-2024 and last for 3 years.

[Requirements for Candidates]

You must be able to demonstrate:

•Master’s degree or a first honours degree (H1 actual, ideally Grade Point Average, GPA of ≥85%) in science, material science or engineering, material physics, chemistry, or related fields (which must have been awarded by the starting date of the PhD)

•The ability of data analysis, scientific writing, and presentations. Be able to use common software (e.g., Microsoft Word, Excel, and PowerPoint).

•Good programming skills (e.g., Python)

•Excellent written and spoken communication skills in English. If your first language is not English, or if your degree qualifications were not undertaken in English, evidence of English proficiency such as a copy of the result of an English test (e.g., IELTS, TOEFL, etc.) is required. For details, please see Step 5 in: https://www.deakin.edu.au/research/research-degrees-and-PhD/research-applications

•Willingness to learn new skills and good at handling pressure.

•Good team spirit (willingness to cooperate).

Desirable special skills:

•Machine learning knowledge and experiences 

•Modelling experience (e.g., Molecular dynamics simulations)

•Prior research experience, especially in science, material science or engineering, material physics, chemistry, or related fields, with a sound record of peer-reviewed publications

•Synthetic laboratory experience including excellent familiarity with safe chemical laboratory practices

[Scholarship details]

•3 years for a stipend and 4 years for the tuition fees offset

•A stipend of $34,400 per annum tax exempt (effective from 28/05/2023)

•For international students only: Single Overseas Student Health Cover policy for the duration of the student visa.

For more details, please see: https://www.deakin.edu.au/study/fees-and-scholarships/scholarships/find-a-scholarship/rtp-and-duprs

[How to apply]

•Please send your CV with details of your qualifications (and grades), publications, and relevant research experience to Dr Hiroyuki Ueda ([email protected]) and Dr Fangfang Chen ([email protected]), or

•Please go to the “Study with us” section on the IFM’s website (https://ifm.deakin.edu.au/study-with-us/) and complete the Expression of Interest (EOI) form (click “Expression of Interest form” to go to a submission page).

[Important dates]

Applications will close when a successful candidate has been identified or internal scholarship slots have been fully allocated, whichever is earlier. If you are interested and meet the above requirements, please apply for this project as soon as possible.

[Inquiry]

Additional information on the position can be acquired via email from Dr Hiroyuki Ueda ([email protected]) and Dr Fangfang Chen ([email protected]).