Quantum Dots (QDs) are versatile nanoscale materials that are increasingly used to boost efficiency in lighting and solar energy conversion devices.
While QDs can be tailored to exhibit desirable opto-electronic properties, their synthesis still requires a lengthy trial-and-error procedure to find the right starting reagents (precursors) and ideal experimental conditions.
In this proposal, we aim to greatly speed up this process by developing a robust and reliable automated screening workflow in which quantum chemical software packages are combined with statistical data analysis tools. Unique and crucial in this approach is the ability to explicitly include the experimental conditions in all stages of the QD synthesis.
In this manner, we create reliable models for which we can design highly parallelized Python workflows to quickly filter out suitable precursors for the preparation of novel QDs.
The machine-learning libraries necessary for statistical analysis and pattern recognition will be deployed inside QMWorks, a Python package constructed to support massively parallel execution of quantum chemical modelling workflows. Using the multiscale modelling facilities in QMWorks, we will be able to avoid redundant calculations and achieve a prediction speed that allows for direct interaction with experimental colleagues that will ultimately test the candidate materials.