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RESEARCH PAPER · Materials · 2023

Application of Machine Learning in Material Synthesis and Property Prediction

Guannan Huang, Yani Guo, Ye Chen, Zhengwei Nie

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pharmacokinetics to improve individual prediction of vancomycin clearance in simulated adult patients15Machine-Learning driven STM images prediction of doped/defective graphene: Towards optimized tools for 2D nanomaterials characterization9Decoding characteristics of key physical properties in silver nanoparticles by attaining centroids for cytotoxicity prediction through data cleansing1A review of emerging trends, challenges, and opportunities for utilization of metal nanoclusters in CO2 capturing3Anti-solvent materials enhanced structural and optical properties on ambiently fabricated perovskite thin films17Predicting battery applications for complex materials based on chemical composition and machine learning14Prediction of crystal structure, phase group, and stability of 2D materials through data science coupled with DFT4Machine Learning as a “Catalyst” for Advancements in Carbon Nanotube Research37Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning25Active learning enabled reactor characterization for mass transfer in aerobic oxidation reactions2Catalytic Reactor-Utilized Ammonia Adsorption, Absorption, and Storage Materials: Mechanism, Nanostructure, and Ab Initio Design27De novo Design of Biocompatible Nanomaterials Using Quasi-SMILES and Recurrent Neural Networks13Extreme high accuracy prediction and design of Fe-C-Cr-Mn-Si steel using machine learning3Inverse prediction of Al alloy post-processing conditions using classification with guided oversampling3The application of machine learning in 3D/4D printed stimuli-responsive hydrogels30A Material Type Prediction Model Based on Machine Learning Technology0Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility17A review of large language models and autonomous agents in chemistry269Property Prediction for Complex Compounds Using Structure-Free Mendeleev Encoding and Machine Learning9State-of-the-art review on various applications of machine learning techniques in materials science and engineering26Transformative strategies in photocatalyst design: merging computational methods and deep learning39An improved machine learning strategy using structural features to predict the glass transition temperature of oxide glasses2Composition-property extrapolation for compositionally complex solid solutions based on word embeddings4Density-aware active learning for materials discovery: a case study on functionalized nanoporous materials3Development of a Ball Mill for High Throughput Studies1Introduction to Machine Learning for Predictive Modeling of Organic Materials0Large Language Model-Driven Knowledge Discovery for Designing Advanced Micro/Nano Electrocatalyst Materials4Machine learning and first-principles calculations for the prediction and analysis of superconductivity in Mg–B–N systems5Machine Learning for Custom-Designed Light Emitters in Biomedicine15Machine Learning-enhanced Copper (I) Thiocyanate-based Perovskite-silicon Tandem Solar Cells: Optimization Strategies for Enhanced Effi ciency and Stability3Magnetization intensity prediction and performance analysis of Fe-based alloys based on machine learning0New Intelligent Computing Techniques for Engineering Materials—A Comprehensive Review0The Problem of Competing Scientific Ontologies in Materials Knowledge Representation0Review of supercooling suppression of phase change materials based on nanoparticles24Machine learning algorithms based approach on prediction of mechanical behaviour of PLA/brass composites manufactured by additive manufacturing6The Convergence of Nanotechnology and Artificial Intelligence: Unlocking Future Innovations15The influence of frequency and temperature on the AC-conductivity in $\text {TlInTe}_2$ semiconductor single crystal14Prediction of the sound absorption performance for micro-perforated panel based on machine learning2Advances in 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