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RESEARCH PAPER · Chinese Journal of Chemistry · 2020

How Machine Learning Accelerates the Development of Quantum Dots? †

Jia Peng, Muhammad Asif Ramzan, Shuliang Wang, Haizheng Zhong

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How Machine Learning Accelerates the Development of Quantum Dots? †OriginalIntelligent control of nanoparticle synthesis through machine learning83Graphene Quantum Dots with Improved Fluorescence Activity via Machine Learning: Implications for Fluorescence Monitoring24Size‐Controllable Eu‐MOFs through Machine Learning Technology: Application for High Sensitive Ions and Small‐Molecular Identification16Enhanced Aggregation-Induced Emission Activity of Metal–Organic Frameworks by Using Machine Learning Technology2Intelligent control of nanoparticle synthesis on microfluidic chips with machine learning102Practices of machine learning in classification of nanomaterial0Review of roll-to-roll fabrication techniques for colloidal quantum dot solar cells37Machine learning for carbon dot synthesis and applications50ML-Based Forecasting of Temporal Dynamics in Luminescence Spectra of Ag 2 S Colloidal Quantum Dots5Rational Design of Full-Color Fluorescent C 3 N Quantum Dots11Deep-learning-based inverse design of colloidal quantum dots13Machine learning enhanced evaluation of semiconductor quantum dots26Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots55Machine Learning Tools to Assist the Synthesis of Antibacterial Carbon Dots26The interface of machine learning and carbon quantum dots: From coordinated innovative synthesis to practical application in water control and electrochemistry88Predicting PbS Colloidal Quantum Dot Solar Cell Parameters Using Neural Networks Trained on Experimental Data6Machine learning in industrialization: a bibliometric analysis1The Convergence of Nanotechnology and Artificial Intelligence: Unlocking Future Innovations15Preparation of sulfur group compounds and perovskite quantum dot films and optimization strategies for efficient light utilization: Recent advances and perspectives7Machine Learning-Driven Optimization of Quantum Dot Bioconjugates for Biomedical Applications0Supervised machine learning prediction and investigation of nonlinear optical rectification in Ge/Si0.15Ge0.85 asymmetric coupled triangle quantum wells10Machine learning models for predicting the hydrogenic impurity nonlinear optical rectification in GaAs/AlGaAs Tetrapod core/shell quantum dots under the effect of temperature4Quantum dot-sensitized solar cells: broader implications for renewable energy solutions from (2020–2025)5Machine learning-based prediction of nonlinear optical rectification in GaAs/AlGaAs tetrapod core/shell quantum dots under pressure and central hydrogenic impurity effects12Deep Learning Models for Colloidal Nanocrystal Synthesis8Recent advances in the nanoparticle-based targeted cancer treatment: A review13Aqueous Continuous Flow Synthesis of Cadmium Chalcogenide Quantum Dots: Opportunities and Challenges2Life Cycle Assessment of Engineered Quantum Dots0Rainfall Forecasting in Jakarta Using Machine Learning and Transformer-Based Time Series Models0AI-driven design and applications of quantum dots9Machine learning and artificial intelligence in perovskite quantum dot electroanalysis: From data-driven synthesis to intelligent sensing interfaces12Predicting Carbon Dot Photoluminescence: A Comparative Machine Learning Study on Systematic Synthesis Data4Machine-Learning-Assisted Carbon Dots: From Algorithms to Applications and Beyond3P‐11.1: AI‐Enabled Automated Synthesis Platform for High‐Throughput and Controlled Growth of CdTe Quantum Dots0Prediction of the absorption coefficient in multi-quantum dot structures using deep neural networks0

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  2. Graphene Quantum Dots with Improved Fluorescence Activity via Machine Learning: Implications for Fluorescence Monitoring

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  3. Size‐Controllable Eu‐MOFs through Machine Learning Technology: Application for High Sensitive Ions and Small‐Molecular Identification

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  4. Enhanced Aggregation-Induced Emission Activity of Metal–Organic Frameworks by Using Machine Learning Technology

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  5. Intelligent control of nanoparticle synthesis on microfluidic chips with machine learning

    NPG Asia Materials · 2022-08-11

    Xueye Chen, Honglin Lv

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  6. Practices of machine learning in classification of nanomaterial

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    Thakar Hitesh Manibhai, Mohit Kumar, S. Jeyalaksshmi, Abdullah, Sakti Charan Panda, Souvik Pal

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  7. Review of roll-to-roll fabrication techniques for colloidal quantum dot solar cells

    Journal of Electronic Science and Technology · 2023-02-28

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  8. Machine learning for carbon dot synthesis and applications

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  9. ML-Based Forecasting of Temporal Dynamics in Luminescence Spectra of Ag 2 S Colloidal Quantum Dots

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    Ivan Pavlovich Malashin, D.S. Daibagya, В С Тынченко, Vladimir A. Nelyub, А. С. Бородулин, Andrei Gantimurov, S. A. Ambrozevich, А.S. Selyukov

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  10. Rational Design of Full-Color Fluorescent C 3 N Quantum Dots

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  11. Deep-learning-based inverse design of colloidal quantum dots

    Optics Communications · 2024-02-17

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  12. Machine learning enhanced evaluation of semiconductor quantum dots

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    Emilio Corcione, Fabian Jakob, Lukas Wagner, Raphael Joos, André Bisquerra, Marcel Schmidt, Andreas Dirk Wieck, Arne Ludwig, Michael Jetter, Simone Luca Portalupi, Peter Michler, Cristina Tarín

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  13. Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots

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  14. Machine Learning Tools to Assist the Synthesis of Antibacterial Carbon Dots

    International Journal of Nanomedicine · 2024-06-01

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  15. The interface of machine learning and carbon quantum dots: From coordinated innovative synthesis to practical application in water control and electrochemistry

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  16. Predicting PbS Colloidal Quantum Dot Solar Cell Parameters Using Neural Networks Trained on Experimental Data

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    Hoon Jeong Lee, Arlene Chiu, Yida Lin, Sreyas M. Chintapalli, Serene Kamal, Eric Ji, Susanna M. Thon

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  17. Machine learning in industrialization: a bibliometric analysis

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  18. The Convergence of Nanotechnology and Artificial Intelligence: Unlocking Future Innovations

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  20. Machine Learning-Driven Optimization of Quantum Dot Bioconjugates for Biomedical Applications

    International Conference on Bioinformatics and Biomedical Technology (ICBBT) · 2025-05-23

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  21. Supervised machine learning prediction and investigation of nonlinear optical rectification in Ge/Si0.15Ge0.85 asymmetric coupled triangle quantum wells

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  22. Machine learning models for predicting the hydrogenic impurity nonlinear optical rectification in GaAs/AlGaAs Tetrapod core/shell quantum dots under the effect of temperature

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  23. Quantum dot-sensitized solar cells: broader implications for renewable energy solutions from (2020–2025)

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