# Machine Learning Models for Accurately Predicting Properties of CsPbCl3 Perovskite Quantum Dots

arXiv: https://arxiv.org/abs/2406.15515 · HTML: https://mlsanity.com/paper/2406.15515/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 45 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [A deep learning system for differential diagnosis of skin diseases](https://mlsanity.com/paper/1909.05382/) (2019)
- [Geometry- and Accuracy-Preserving Random Forest Proximities](https://mlsanity.com/paper/2201.12682/) (2022)
- [Random Forest for Label Ranking](https://mlsanity.com/paper/1608.07710/) (2016)
- [GrCAN: Gradient Boost Convolutional Autoencoder with Neural Decision Forest](https://mlsanity.com/paper/1806.08079/) (2018)
- [Neural network algorithm and its application in temperature control of distillation tower](https://mlsanity.com/paper/2101.00582/) (2021)

## Abstract (arXiv)

Perovskite Quantum Dots (PQDs) have a promising future for several applications due to their unique properties. This study investigates the effectiveness of Machine Learning (ML) in predicting the size, absorbance (1S abs) and photoluminescence (PL) properties of CsPbCl3 PQDs using synthesizing features as the input dataset. the study employed ML models of Support Vector Regression (SVR), Nearest Neighbour Distance (NND), Random Forest (RF), Gradient Boosting Machine (GBM), Decision Tree (DT) and Deep Learning (DL). Although all models performed highly accurate results, SVR and NND demonstrated the best accurate property prediction by achieving excellent performance on the test and training datasets, with high R2 and low Root Mean Squared Error (RMSE) and low Mean Absolute Error (MAE) metric values. Given that ML is becoming more superior, its ability to understand the QDs field could prove invaluable to shape the future of nanomaterials designing.