Sign in

eScience Technology to Boost Quantum Dot Energy Conversion

More efficient lighting and solar energy conversion devices

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.

Participating organisations

Natural Sciences & Engineering
Natural Sciences & Engineering
Netherlands eScience Center
Vrije Universiteit Amsterdam

Output

Team

BvB
Bas van Beek
PhD student
Vrije Universiteit Amsterdam
FZ
Francesco Zaccaria
Co-Applicant
Istituto Italiano di Tecnologia
RP
Roberta Pascazio
PhD student
Istituto Italiano di Tecnologia
LV
Luuk Visscher
Co-Applicant
Vrije Universiteit Amsterdam
JZ
Juliette Zito
PhD candidate
Istituto Italiano di Tecnologia
AJ
Aron Jansen
eScience Research Engineer
Netherlands eScience Center
Felipe Zapata
Felipe Zapata
eScience Research Engineer
Netherlands eScience Center
II
Ivan Infante
Principal investigator
Vrije Universiteit Amsterdam
Nicolas Renaud
Nicolas Renaud
eScience Coordinator
Netherlands eScience Center
Rena Bakhshi
Rena Bakhshi
Programme Manager
Netherlands eScience Center

Related projects

DIANNA - Deep Insight and Neural Networks Analysis

Explainable AI tool for scientists

Updated 8 months ago
Running

A light in the dark

Quantum Monte Carlo meets solar energy conversion

Updated 9 months ago
Finished

A phase field model to guide the development and design of next generation solid-state-batteries

Safer batteries with higher energy densities

Updated 9 months ago
Finished

Computation of the Optical Properties of nano structures

Accurate and Efficient Computation of the Optical Properties of Nanostructures for Improved Photovoltaics

Updated 9 months ago
Finished

MULTIXMAS

Multiscale simulations of excitation dynamics in molecular materials for sustainable energy applications

Updated 9 months ago
Finished

Passing XSAMS

New tools for researchers in plasma, combustion and chemical reactor science

Updated 9 months ago
Finished

Scalable high-fidelity simulations of reacting multiphase flows at transcritical pressure

Solving a scalability problem through dynamic multi-level parallelization

Updated 9 months ago
Finished

PROMIMOOC

Process mining for multi-objective online control

Updated 9 months ago
Finished

Related tools

Ceiba

CE

Ceiba and its command-line interface Ceiba-cli solve the problem of computing, storing, and securely sharing computationally expensive simulation results.

Updated 14 months ago
2

DIANNA

DI

Deep Insight And Neural Network Analysis, DIANNA is the only Explainable AI, XAI library for scientists supporting Open Neural Network Exchange, ONNX - the de facto standard models format.

Updated 3 months ago
12 11

flamingo

FL

Compute and filter molecular properties for quantum dot ligands

Updated 14 months ago
1

Swan

SW

Swan is a Python package to create statistical models to predict molecular properties

Updated 14 months ago
1