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RESEARCH PAPER · Chemistry of Materials · 2022

Predicting Indium Phosphide Quantum Dot Properties from Synthetic Procedures Using Machine Learning

Hao Nguyen, Florence Y. Dou, Nayon Park, Shenwei Wu, Harrison Sarsito, Benedicte Diakubama, Helen C. Larson, Emily Nishiwaki, Micaela Homer, Melanie Cash, Brandi Michelle Cossairt

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Predicting Indium Phosphide Quantum Dot Properties from Synthetic Procedures Using Machine LearningOriginalBlue-emitting InP quantum dots participate in an efficient resonance energy transfer process in water41Colloidal III–V quantum dots: a synthetic perspective22Synthesis, Surface Chemistry, and Fluorescent Properties of InP Quantum Dots101Electronic Lab Notebooks for Materials Synthesis6Closed-loop optimization of nanoparticle synthesis enabled by robotics and machine learning72Machine Learning-Directed Predictive Models: Deciphering Complex Energy Transfer in Mn-Doped CsPb(Cl 1– y Br y ) 3 Perovskite Nanocrystals21Machine learning in analytical chemistry: From synthesis of nanostructures to their applications in luminescence sensing75Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis33Machine learning predicted emission of water-stable CdTe quantum dots4Machine learning–assisted colloidal synthesis: A review39ML-Based Forecasting of Temporal Dynamics in Luminescence Spectra of Ag 2 S Colloidal Quantum Dots5The Challenge of Colloidal Nanoparticle Synthesis1Understanding Hot Injection Quantum Dot Synthesis Outcomes Using Automated High-Throughput Experiment Platforms and Machine Learning24Deep-learning-based inverse design of colloidal quantum dots13Machine learning enhanced evaluation of semiconductor quantum dots26Optimizing the Synthesis Parameters of Double Perovskites with Machine Learning Using a Multioutput Regression Model10Predicting PbS Colloidal Quantum Dot Solar Cell Parameters Using Neural Networks Trained on Experimental Data6Core‐Shell Colloidal Quantum Dots for Energy Conversion48Machine-Learning-Driven Predictive 3D Ramified Foam Fabrication and Mechanistic Understanding1Cation engineering modified InP quantum dots for enhanced properties and diversified applications26From synthesis to properties: expanding the horizons of machine learning in nanomaterials research39Indium phosphide quantum dots as green nanosystems for environmental detoxification: surface engineering, photocatalytic mechanisms, and comparative material insights16Optimization and Interpretability Analysis of Machine Learning Methods for ZnO Colloid Particle Size Prediction4Principles and Practices for Quantum Dots Synthesis0Advancing Ecofriendly Indium Phosphide Quantum Dots: Comprehensive Strategies toward Color-Pure Luminescence for Wide Color Gamut Displays52Advances in relaxation and memory effects of magnetic nanoparticles for biomedical applications17Machine Learning Driven Energy Transfer Prediction in Mn-Doped 2D Halide Perovskites5Machine learning models for accurately predicting properties of CsPbCl3 Perovskite quantum dots10Prospects of Nanoscience with Nanocrystals: 2025 Edition59Expanding Parameter Space to Enable Low-Temperature Synthesis of Crystalline Indium Phosphide Quantum Dots10Indium phosphide quantum dots in environmental photoelectrochemical systems: advanced synthesis, surface engineering, and analytical applications1Deep Learning Models for Colloidal Nanocrystal Synthesis8Accelerated discovery of multinary chalcogenide quantum dots: combining aqueous chemistry with high-throughput experimentation and machine learning3Engineering indium phosphide quantum dots for solar-driven energy conversion applications8Indium phosphide quantum dots: advanced synthesis, surface engineering, and biomedical applications in imaging, sensing, and therapy10Quantum Dots: Transforming Cancer Diagnosis and Treatment Through Advanced Theranostics0AI-driven design and applications of quantum dots9Machine learning and artificial intelligence in perovskite quantum dot electroanalysis: From data-driven synthesis to intelligent sensing interfaces12Integrating Machine Learning and Life Cycle Assessment for Fehling-Inspired Sustainable Synthesis of Cu 2 O Nanowires and ML-Guided Design of Metal Oxide Nanorods1From Discovery to Deliverables: Bridging the Machine Learning, Synthesis, and Characterization Gap in Nanoscale Materials1Synthesis, optical properties, and biological applications of luminescent nanomaterials: Paradigm shift towards safer semiconductor quantum dots and nanoparticles2A Large-Scale Nanocrystal Database with Aligned Synthesis and Properties, Enabling Generative Inverse Design0Indium phosphide quantum dots for photocatalysis and solar energy conversion: Materials design and device integration0Chemical physics of surface reconstruction and defect passivation in InP quantum dots: Thermodynamic mechanisms, electronic structure evolution, and implications for high-performance optoelectronics0Applying the CRADLE Method: An Undergraduate Case Study on Machine Learning for Chiral Noble Metal Nanoparticles Using Literature-Mined Data0Machine Learning Enhanced Quantum Dot Solar Cells: Accelerating Material Discovery and Device Optimization0Thermodynamic insights into ligand exchange and surface defect passivation of indium phosphide quantum dots for biomedical interface engineering0

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  1. Blue-emitting InP quantum dots participate in an efficient resonance energy transfer process in water

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  2. Colloidal III–V quantum dots: a synthetic perspective

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  3. Synthesis, Surface Chemistry, and Fluorescent Properties of InP Quantum Dots

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  4. Electronic Lab Notebooks for Materials Synthesis

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  5. Closed-loop optimization of nanoparticle synthesis enabled by robotics and machine learning

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  6. Machine Learning-Directed Predictive Models: Deciphering Complex Energy Transfer in Mn-Doped CsPb(Cl 1– y Br y ) 3 Perovskite Nanocrystals

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  7. Machine learning in analytical chemistry: From synthesis of nanostructures to their applications in luminescence sensing

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  8. Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis

    Inorganic Chemistry · 2023-09-28

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  9. Machine learning predicted emission of water-stable CdTe quantum dots

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    André F. V. Fonseca, Cintia Ellen Giarola, Thaís Adriany de Souza Carvalho, Fernanda Sumika Hojo de Souza, Marco Antônio Schiavon

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  10. Machine learning–assisted colloidal synthesis: A review

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  11. 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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  12. The Challenge of Colloidal Nanoparticle Synthesis

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  13. Understanding Hot Injection Quantum Dot Synthesis Outcomes Using Automated High-Throughput Experiment Platforms and Machine Learning

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

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  15. Machine learning enhanced evaluation of semiconductor quantum dots

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  16. Optimizing the Synthesis Parameters of Double Perovskites with Machine Learning Using a Multioutput Regression Model

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

    Advanced Intelligent Systems · 2024-09-09

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  18. Core‐Shell Colloidal Quantum Dots for Energy Conversion

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  19. Machine-Learning-Driven Predictive 3D Ramified Foam Fabrication and Mechanistic Understanding

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  20. Cation engineering modified InP quantum dots for enhanced properties and diversified applications

    Coordination Chemistry Reviews · 2024-12-05

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  21. From synthesis to properties: expanding the horizons of machine learning in nanomaterials research

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  22. Indium phosphide quantum dots as green nanosystems for environmental detoxification: surface engineering, photocatalytic mechanisms, and comparative material insights

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  23. Optimization and Interpretability Analysis of Machine Learning Methods for ZnO Colloid Particle Size Prediction

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  24. Principles and Practices for Quantum Dots Synthesis

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  25. Advancing Ecofriendly Indium Phosphide Quantum Dots: Comprehensive Strategies toward Color-Pure Luminescence for Wide Color Gamut Displays

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  27. Machine Learning Driven Energy Transfer Prediction in Mn-Doped 2D Halide Perovskites

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