Machine Learning Force Fields for Colloidal Quantum Dots
Mario Fernández-Pendás a, Ivan Infante a b
a BCMaterials, Basque Center for Materials, Applications and Nanostructures, Bld. Martina Casiano, 3rd. Floor, UPV/EHU Science Park, 48940, Leioa, Spain
b Ikerbasque, Basque Foundations for Science, María Díaz de Haro 3, 48013 Bilbao, Spain.
Materials for Sustainable Development Conference (MATSUS)
Proceedings of MATSUS Spring 2024 Conference (MATSUS24)
#AI - Automation and Nanomaterials (machine learning, artificial intelligence, robotics, accelerated discovery)
Barcelona, Spain, 2024 March 4th - 8th
Organizers: Ivan Infante and Oleksandr Voznyy
Oral, Mario Fernández-Pendás, presentation 385
DOI: https://doi.org/10.29363/nanoge.matsus.2024.385
Publication date: 18th December 2023

Colloidal quantum dots (QDs) and in particular perovskite QDs have attracted broad interest for their highly efficient, spectrally tunable and narrowband photoluminescence (PL). Their potential implementation in a plethora of nanomaterials technologies spanning efficient lighting and light harvesting to bioimaging, demand a thorough understanding of the effects of QD structure and surface structure dynamics on PL characteristics. In principle, low-cost simulations with classical force fields (FFs) reveal no electronic structure information and thus provide restricted insight into PL features. Enabling molecular dynamics (MD) simulations in both the ground and excited states with electronic structure information up to the nanosecond timescale is a desirable advance.

For this purpose, we propose a customized platform that helps constructing machine learning FFs for colloidal QDs, expanding the already existing platform dedicated to the parameterization of classical FF parameters for QDs. By considering quantum mechanical properties (such as potential energies and bandgaps) previously computed at a high-level of theory like density functional theory (DFT), along with position and forces, this platform automatically trains a neural network to generate the so-called machine learning FFs (MLFF). These MLFFs not only allow to expand the timescale of the MD simulations from picoseconds to nanosecond, but will also enhance sampling efficiency and simulation accuracy by including also information on the electronic density of states at each point of the trajectory. The machine learning platform has been tested for CsPbBr3 QDs obtaining promising results.

M.F.P.'s is funded by the IKUR's project titlted "A GeneRic Automated and Adaptive machine Learning platform for advanced materials research-implement the machine learning methods in the automate platform".

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