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RESEARCH PAPER · Materials Today · 2020

Machine learning-guided synthesis of advanced inorganic materials

Bijun Tang, Yuhao Lu, Jiadong Zhou, Tushar Chouhan, Han Wang, Prafful Golani, Manzhang Xu, Manzhang Xu, Quan Xu, Cuntai Guan, Zheng Liu

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Machine learning-guided synthesis of advanced inorganic materialsOriginalMachine-Learning-Driven Synthesis of Carbon Dots with Enhanced Quantum Yields274Chemical Vapor Deposition Growth of Two-Dimensional Compound Materials: Controllability, Material Quality, and Growth Mechanism271Machine learning for advanced energy materials208Efficient sampling for decision making in materials discovery*10Study on strengthening effects of Zr-Ti-Nb-O alloys via high throughput powder metallurgy and data-driven machine learning29Accelerated discovery of single-phase refractory high entropy alloys assisted by machine learning98Crystal-Site-Based Artificial Neural Networks for Material Classification5An emerging machine learning strategy for the assisted‐design of high-performance supercapacitor materials by mining the relationship between capacitance and structural features of porous carbon74Recent machine learning guided material research - A review12Machine Learning Driven Synthesis of Few-Layered WTe2 with Geometrical Control68Machine Learning-Assisted Development of Sensitive Electrode Materials for Mixed Potential-Type NO2Gas Sensors34Novel Materials for Urban Farming91Predicting synthesizability of crystalline materials via deep learning43On-the-fly interpretable machine learning for rapid discovery of two-dimensional ferromagnets with high Curie temperature92Intelligent control of nanoparticle synthesis through machine learning83Intelligent Design of High Strength and High Conductivity Copper Alloys Using Machine Learning Assisted by Genetic Algorithm9Machine learning guided full-color V 4 C 3 MXene quantum dots for building WLEDs20Machine learning guided microwave-assisted quantum dot synthesis and an indication of residual H 2 O 2 in human teeth35Machine Learning Utilized for the Development of Proton Exchange Membrane Electrolyzers51Machine learning-driven advanced development of carbon-based luminescent nanomaterials23Synthetic Protocols0Machine Learning Guided Synthesis of Flash Graphene110Prediction of concrete strengths enabled by missing data imputation and interpretable machine learning218Chemical vapor deposition of 2D materials: A review of modeling, simulation, and machine learning studies140Contribution of nicotinamide as an intracyclic N dopant to the structure and properties of carbon dots synthesized using three α -hydroxy acids as C sources5Application of machine learning for advanced material prediction and design123Perovskite synthesizability using graph neural networks80Continuous Homogeneous Catalytic Oxidation of C–H Bonds by Metal-Free Carbon Dots with a Poly(ascorbic acid) Structure25Hybrid DFT/Data-Driven Approach for Searching for New Quasicrystal Approximants in Sc-X (X = Rh, Pd, Ir, Pt) Systems9Recent Developments in Chemical Vapor Deposition of 2D Magnetic Transition Metal Chalcogenides19Recent Advances in the Development of Metal–Organic Frameworks for Propylene and Propane Separation49Strategies for Controlled Growth of Transition Metal Dichalcogenides by Chemical Vapor Deposition for Integrated Electronics96Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions48Intelligent control of nanoparticle synthesis on microfluidic chips with machine learning102The intrinsic strength prediction by machine learning for refractory high entropy alloys25Development of machine learning models to enhance element-doped g-C3N4 photocatalyst for hydrogen production through splitting water44Feature Fusion Deep Learning Model for Defects Prediction in Crystal Structures4Controllable Synthesis of Novel Two-Dimensional Nonlayered β-Bi2Te4O11 Flakes for Promising Optoelectronic Applications10Overview: recent studies of machine learning in phase prediction of high entropy alloys56Machine learned synthesizability predictions aided by density functional theory30Machine-learning accelerated identification of exfoliable two-dimensional 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structural stability of Mn-based ion sieves for Lithium extraction from brine2Review on Aging of Biodegradable Polymers: Experimental Approaches and Emerging Role of Machine and Deep Learning5Artificial intelligence-enabled chiral functional materials design4Advancing carbon dots research with machine learning: a comprehensive review4Machine learning‐driven advances in carbon‐based quantum dots: Opportunities accompanied by challenges6Machine Learning Guided Growth of Ultrathin 2D Fe 3 O 4 With a Single Magnetic Domain0Artificial Intelligence-Driven Wearable Sensors for Cardiovascular Health Monitoring6From Discovery to Deliverables: Bridging the Machine Learning, Synthesis, and Characterization Gap in Nanoscale Materials1Carbon quantum dots from waste and biomass for water treatment: A review on nanomaterials AI-enhanced synthesis and characterization3Surface Engineering of Zinc Anodes toward Durable Aqueous Zinc‐Ion Batteries8Transforming waste into innovation: Biowaste-derived quantum dots for future technologies2Machine-Learning-Assisted Carbon Dots: From Algorithms to Applications and Beyond3Artificial Intelligence for Materials Science: Transforming Research Paradigms6Machine learning-guided inverse design of biomass-derived MgO nanoparticles with targeted sizes: A case study on As(III) adsorption1

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