The discovery of new materials has fueled the most relevant technological advances across history. Advances in modern chemistry and physics, combined with nanotechnology have enabled a rapid growth in the knowledge and utilization of emerging materials, offering guidance into their design from the atomic level, synthesis, and discovery of new classes of materials.

There are virtually infinite combinations of materials in terms of composition and structure. To date, the discovery of new materials has been based on serendipity, empirical observations, or time-consuming computational modeling. In this project, we will utilize artificial intelligence to accelerate the design of new materials for energy harvesting and storage applications.

Objectives:
This thesis will focus on the automated synthesis of solution processed materials that have potential for energy and information technology applications such as perovskites and sol-gels. The candidate will lead or contribute to the following: 1) using robotized platforms, program the synthesis of perovskite and sol-gel-based materials; 2) automate the analysis of their resulting materials, including structure and composition; 3) automate the analysis of the materials properties (e.g., bandgap, complex permittivity, carrier density/mobility) related to a target application (e.g., energy storage, energy harvesting, information storage and processing); 4) implement models that, using these information, design the next experimental step, ultimately enabling the realization of self-driving unsupervised laboratories.


Depending on the candidate’s expertise and interests, the project will be designed to contribute to a subset of the above.

Prospective students should ideally have background on some of the next:
- Coding, data science, analysis, and artificial intelligence
- Computational modelling of materials
- Mechatronics, automation of synthesis and characterization
- Wet-chemistry and solution processed materials
- Materials characterization