Related references
Note: Only part of the references are listed.In Situ Process Monitoring and Multichannel Imaging for Vacuum-Assisted Growth Control of Inkjet-Printed and Blade-Coated Perovskite Thin-Films
Fabian Schackmar et al.
ADVANCED MATERIALS TECHNOLOGIES (2023)
Half and full solar cell efficiency binning by deep learning on electroluminescence images
Yoann Buratti et al.
PROGRESS IN PHOTOVOLTAICS (2022)
Discovery of Lead-Free Perovskites for High-Performance Solar Cells via Machine Learning: Ultrabroadband Absorption, Low Radiative Combination, and Enhanced Thermal Conductivities
Xia Cai et al.
ADVANCED SCIENCE (2022)
An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles
T. Jesper Jacobsson et al.
NATURE ENERGY (2022)
Quantitative Predictions of Moisture-Driven Photoemission Dynamics in Metal Halide Perovskites via Machine Learning
John M. Howard et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2022)
Advanced analytics on IV curves and electroluminescence images of photovoltaic modules using machine learning algorithms
Vedant Kumar et al.
PROGRESS IN PHOTOVOLTAICS (2022)
Anomaly detection in IR images of PV modules using supervised contrastive learning
Lukas Bommes et al.
PROGRESS IN PHOTOVOLTAICS (2022)
Correlative In Situ Multichannel Imaging for Large-Area Monitoring of Morphology Formation in Solution-Processed Perovskite Layers
Simon Ternes et al.
SOLAR RRL (2022)
Efficient Light Harvesting in Thick Perovskite Solar Cells Processed on Industry-Applicable Random Pyramidal Textures
Ahmed Farag et al.
ACS APPLIED ENERGY MATERIALS (2022)
Scalable two-terminal all-perovskite tandem solar modules with a 19.1% efficiency
Bahram Abdollahi Nejand et al.
NATURE ENERGY (2022)
Opportunities for machine learning to accelerate halide-perovskite commercialization and scale-up
Rishi E. Kumar et al.
MATTER (2022)
Perovskite Solar Cells with All-Inkjet-Printed Absorber and Charge Transport Layers
Fabian Schackmar et al.
ADVANCED MATERIALS TECHNOLOGIES (2021)
Using In Situ Optical Spectroscopy to Elucidate Film Formation of Metal Halide Perovskites
Konstantin Schoetz et al.
JOURNAL OF PHYSICAL CHEMISTRY A (2021)
Machine learning for perovskite materials design and discovery
Qiuling Tao et al.
NPJ COMPUTATIONAL MATERIALS (2021)
Segmentation of cell-level anomalies in electroluminescence images of photovoltaic modules
Urtzi Otamendi et al.
SOLAR ENERGY (2021)
Graph representational learning for bandgap prediction in varied perovskite crystals
Pravan Omprakash et al.
COMPUTATIONAL MATERIALS SCIENCE (2021)
Machine Learning Roadmap for Perovskite Photovoltaics
Meghna Srivastava et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2021)
Computer vision tool for detection, mapping, and fault classification of photovoltaics modules in aerial IR videos
Lukas Bommes et al.
PROGRESS IN PHOTOVOLTAICS (2021)
Predicting Perovskite Performance with Multiple Machine-Learning Algorithms
Ruoyu Li et al.
CRYSTALS (2021)
A data fusion approach to optimize compositional stability of halide perovskites
Shijing Sun et al.
MATTER (2021)
Machine learning analysis on stability of perovskite solar cells
Cagla Odabasi et al.
SOLAR ENERGY MATERIALS AND SOLAR CELLS (2020)
Inkjet-Printed Micrometer-Thick Perovskite Solar Cells with Large Columnar Grains
Helge Eggers et al.
ADVANCED ENERGY MATERIALS (2020)
Optical Absorption-Based In Situ Characterization of Halide Perovskites
Finn Babbe et al.
ADVANCED ENERGY MATERIALS (2020)
Forecasting the Decay of Hybrid Perovskite Performance Using Optical Transmittance or Reflected Dark-Field Imaging
Ryan J. Stoddard et al.
ACS ENERGY LETTERS (2020)
Machine-learning structural and electronic properties of metal halide perovskites using a hierarchical convolutional neural network
Wissam A. Saidi et al.
NPJ COMPUTATIONAL MATERIALS (2020)
Database of Two-Dimensional Hybrid Perovskite Materials: Open-Access Collection of Crystal Structures, Band Gaps, and Atomic Partial Charges Predicted by Machine Learning
Ekaterina Marchenko et al.
CHEMISTRY OF MATERIALS (2020)
Designing and understanding light-harvesting devices with machine learning
Florian Hase et al.
NATURE COMMUNICATIONS (2020)
Machine learning for halide perovskite materials
Lei Zhang et al.
NANO ENERGY (2020)
Optical in situ monitoring during the synthesis of halide perovskite solar cells reveals formation kinetics and evolution of optoelectronic properties
Klara Suchan et al.
JOURNAL OF MATERIALS CHEMISTRY A (2020)
Machine-Learning-Accelerated Perovskite Crystallization
Jeffrey Kirman et al.
MATTER (2020)
Identifying Pb-free perovskites for solar cells by machine learning
Jino Im et al.
NPJ COMPUTATIONAL MATERIALS (2019)
Coated and Printed Perovskites for Photovoltaic Applications
Ian A. Howard et al.
ADVANCED MATERIALS (2019)
High-throughput Computational Study of Halide Double Perovskite Inorganic Compounds
Yao Cai et al.
CHEMISTRY OF MATERIALS (2019)
Automated Pipeline for Photovoltaic Module Electroluminescence Image Processing and Degradation Feature Classification
Ahmad Maroof Karimi et al.
IEEE JOURNAL OF PHOTOVOLTAICS (2019)
Recent advances and applications of machine learning in solid-state materials science
Jonathan Schmidt et al.
NPJ COMPUTATIONAL MATERIALS (2019)
Imaging Spatial Variations of Optical Bandgaps in Perovskite Solar Cells
Boyi Chen et al.
ADVANCED ENERGY MATERIALS (2019)
Searching for Hidden Perovskite Materials for Photovoltaic Systems by Combining Data Science and First Principle Calculations
Keisuke Takahashi et al.
ACS PHOTONICS (2018)
Machine learning for molecular and materials science
Keith T. Butler et al.
NATURE (2018)
Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing
Juan-Pablo Correa-Baena et al.
JOULE (2018)
Monitoring Thermal Annealing of Perovskite Solar Cells with In Situ Photoluminescence
Jacobus J. van Franeker et al.
ADVANCED ENERGY MATERIALS (2017)
Promises and challenges of perovskite solar cells
Juan-Pablo Correa-Baena et al.
SCIENCE (2017)
Machine learning in materials informatics: recent applications and prospects
Rampi Ramprasad et al.
NPJ COMPUTATIONAL MATERIALS (2017)
Machine-learning-assisted materials discovery using failed experiments
Paul Raccuglia et al.
NATURE (2016)
Finding New Perovskite Halides via Machine Learning
Ghanshyam Pilania et al.
FRONTIERS IN MATERIALS (2016)
Machine learning bandgaps of double perovskites
G. Pilania et al.
SCIENTIFIC REPORTS (2016)
Perspective: Materials informatics and big data: Realization of the fourth paradigm of science in materials science
Ankit Agrawal et al.
APL MATERIALS (2016)
Perovskites: The Emergence of a New Era for Low-Cost, High-Efficiency Solar Cells
Henry J. Snaith
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2013)
AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
Stefano Curtarolo et al.
COMPUTATIONAL MATERIALS SCIENCE (2012)
The Computational Materials Repository
David D. Landis et al.
COMPUTING IN SCIENCE & ENGINEERING (2012)
Materials informatics
Krishna Rajan
MATERIALS TODAY (2005)