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RESEARCH PAPER · Journal of Materials Chemistry A · 2024

Development and application of Few-shot learning methods in materials science under data scarcity

Yongxing Chen, Long Peng, Bin Liu, Yi Wang, Junlong Wang, Tian Ma, Huilin Wei, Yue Kang, Haining Ji

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42 direct citing works

Development and application of Few-shot learning methods in materials science under data scarcityOriginalHarnessing the leading edge: machine learning ventures in chemistry and materials science3Inverse design of multilayer film compatible with infrared stealth and radiative heat dissipation through deep reinforcement learning11Machine learning-driven soft plasma etching for precision structuring of biofunctional organic semiconductor films2Machine-learning guided discovery of ultralow-threshold organic gain materials towards electrically pumped lasing0Machine-learning-assisted design of energy-saving windows with high near-infrared shielding properties5Bayesian Meta‐Learning for Few‐Shot Reaction Outcome Prediction of Asymmetric Hydrogenation of Olefins2Bayesian Meta‐Learning for Few‐Shot Reaction Outcome Prediction of Asymmetric Hydrogenation of Olefins1Enhancing spatiotemporal wind power forecasting with meta-learning in data-scarce environments10Deep Reinforcement Learning-Guided Inverse Design of Transparent Heat Mirror Film for Broadband Spectral Selectivity3AI ‐Driven Discovery of High Performance Polymer Electrodes for Next‐Generation Batteries10What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials2Enhancing body fat prediction with WGAN-GP data augmentation and XGBoost algorithm6Construction of Cr4+: YAG Crystal Growth Parameter Optimization Model Integrating Meta-Learning Mechanism0QC-Augmented NN/GNN for Few-Shot Prediction of Amorphous Polymer Properties Via Multi-Scale Microstructures1Transformative applications of artificial intelligence in lithium battery materials science: advancements and future prospects0A call to elevate the role of processing in AI-driven materials design8Accelerating the discovery of high-efficiency donor–acceptor pairs in organic photovoltaics via SolarPCE-Net guided screening2AI‐Assisted Design of Advanced Polymeric Materials: Challenges and Solutions25AI-powered language models for alloy design and laser-based manufacturing: A review of NLP applications in materials science16Broadband solar absorber and high-temperature thermal emitter based on deep-learning optimization and design2Cognitive AI beyond prediction: toward reasoning and discovery1Controlled growth of 2D ferrimagnetic vertical Cr 2 S 3 nanosheets and rapid thickness identification0Long photocarrier lifetimes in CsPbBr 3 films deposited by a machine-learning-assisted IR-laser molecular beam epitaxy process1Radiative cooling: a zero-energy thermal management strategy for next-generation electronic devices3Smart window films with ultra-broadband radiative regulation for all-season building energy saving0VO2-based dynamic infrared modulation: properties and applications7Zero-shot evidence-grounded extraction of blood–brain barrier nanoparticle design parameters with open-weight language models0Data-driven approaches for functional ceramics3Machine learning toward electrochemical energy storage materials and devices: A review5High‐Performance Infrared Nonlinear Optical Crystals Discovery Guided by High‐Throughput Computation, Machine Learning, and Experimental Verification6High‐Performance Infrared Nonlinear Optical Crystals Discovery Guided by High‐Throughput Computation, Machine Learning, and Experimental Verification3Small-data machine learning for resolving degradation challenges in energy devices2Machine learning-driven photothermal synergistic optimization of VO2-based composite films for smart windows2Multi-task learning assisted in predicting and optimizing properties of biochar derived from varying biomass wastes for soil applications0Violation detection in power operation sites based on multi-scale detection and few-shot learning0Machine Learning in Multispectral Compatible Camouflage: From Traditional Materials to Intelligent Design0Visualization-driven physics-informed modeling of energetics with demonstrated transfer between configurationally isomorphic substitutional alloys0Machine Learning for Gas Capture in Ionic Liquids: Current Status and Future Trends0Closed-loop design of 300 °C Al-Cu alloys via cross-condition knowledge transfer under extreme data scarcity0Leveraging large language models to address data scarcity in machine learning for graphene synthesis1Multi-objective Prediction and Interpretability Analysis of Mechanical Properties for HRB500E Based on Deep Learning0Machine learning in additive manufacturing of aluminum alloys: a review of fundamentals, challenges, and evolution of intelligent pathways0Research Progress on VO2-Based Smart Windows: From Phase Transition Mechanisms to Performance Regulation0Multilayer Composite Structured Transparent Infrared-Selective Stealth Films with Synergistic Radiative Cooling0Heterogeneous phase-change device for multispectral programmable camouflage and thermal management0Beyond single-membrane models: An explainable machine learning framework for cross-membrane comparison0Towards system-level artificial intelligence in perovskite photovoltaics0

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