In data science we trust: Machine learning for stable halide perovskites-Reference-Cited by-同舟云学术

In data science we trust: Machine learning for stable halide perovskites

Published:2021-04 Issue:4 Volume:4 Page:1092-1094
ISSN:2590-2385
Container-title:Matter
language:en
Short-container-title:Matter

Author:

Ranke Daniel,Perini Carlo Andrea Riccardo,Correa-Baena Juan-Pablo

Publisher

Elsevier BV

Subject

General Materials Science

Reference8 articles.

1. Exploration of the compositional space for mixed lead halogen perovskites for high efficiency solar cells;Jesper Jacobsson;Energy Environ. Sci.,2016

2. Suppression of atomic vacancies via incorporation of isovalent small ions to increase the stability of halide perovskite solar cells in ambient air;Saidaminov;Nat. Energy,2018

3. Machine Learning for Tailoring Optoelectronic Properties of Single-Walled Carbon Nanotube Films;Khabushev;J. Phys. Chem. Lett.,2019

4. An Adaptive Machine Learning Strategy for Accelerating Discovery of Perovskite Electrocatalysts;Li;ACS Catal.,2020

5. Critical temperature prediction for a superconductor: A variational bayesian neural network approach;Dung Le;IEEE Transactions on Applied Superconductivity,2020

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1. Entropy-metric estimation of the small data models with stochastic parameters;Heliyon;2024-01

2. High-throughput calculation and machine learning of two-dimensional halide perovskite materials: Formation energy and band gap;Materials Today Communications;2023-06

3. Machine learning methods for aerosol synthesis of single-walled carbon nanotubes;Carbon;2023-01

4. Recent progress and future prospects on halide perovskite nanocrystals for optoelectronics and beyond;iScience;2022-11

5. Interpretable Machine Learning for Investigating Photoelectrochemical Properties of Cosensitizer-Based CH₃NH₃PbI₃/TiO₂ Films in Water;The Journal of Physical Chemistry C;2022-03-31