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RESEARCH PAPER · Nature Communications · 2023

Exploiting redundancy in large materials datasets for efficient machine learning with less data

Kangming Li, Daniel Persaud, Kamal Kumar Choudhary, Brian DeCost, Michael Greenwood, Jason R. Hattrick-Simpers

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Exploiting redundancy in large materials datasets for efficient machine learning with less dataOriginalBenchmarking Without Reality: Dataset Construction Bias in Materials Evaluation0Computational and Data-Driven Materials Engineering: High-Throughput Computational Screening Platforms, Workflows, and Discovery Outcomes0Driving school for self-driving labs17ColabFit exchange: Open-access datasets for data-driven interatomic potentials20Multi-principal element alloy discovery using directed energy deposition and machine learning35A call for caution in the era of AI-accelerated materials science2Realistic material property prediction using domain adaptation based machine learning48Not as simple as we thought: a rigorous examination of data aggregation in materials informatics13A reproducibility study of atomistic line graph neural networks for materials property prediction5Algorithmic Consensus vs Scientific Consensus in Materials Prediction0Efficient first principles based modeling via machine learning: from simple representations to high entropy materials23Topological Quality of Subsets Via Persistence Matching Diagrams0Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning70Sample Size Requirements of a Pharmaceutical Material Library: A Case in Predicting Direct Compression Tablet Tensile Strength by Latent Variable Modeling3Optimized Bags of Artificial Neural Networks Can Predict the Viability of Organisms Exposed to Nanoparticles6Key requirements for advancing machine learning approaches in single entity electrochemistry10Conceptualizing future groundwater models through a ternary framework of multisource data, human expertise, and machine intelligence75Machine learning assisted mechanical properties prediction of fine denier polyester fiber5JARVIS-Leaderboard: a large scale benchmark of materials design methods65High-throughput discovery of metal oxides with high thermoelectric performance via interpretable feature engineering on small data3Roadmap on data-centric materials science27Cluster-Based Optimization of Training Data Selection for Image Copy Detection Models1Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study66Integration of artificial intelligence and big data in materials science: New paradigms and scientific discoveries2Embedding human knowledge in material screening pipeline as filters to identify novel synthesizable inorganic materials4Machine learning-assisted wood materials: Applications and future prospects22A data-driven method to construct prediction model of solar stills7Machine Learning Interatomic Potentials for Heterogeneous Catalysis43Intelligent void identification of particle packing system of caved ore and rock3Thermal Metamaterials with Configurable Mechanical Properties15Interpretable Machine Learning for Investigating the Molecular Mechanisms Governing the Transparency of Colorless Transparent Polyimide for OLED Cover Windows33$\gamma$-Razor: Hardness-Aware Dataset Pruning for Efficient Neural Network Training3MD-HIT: Machine learning for material property prediction with dataset redundancy control53Deep Learning-Based In Situ Micrograph Synthesis and Augmentation for Crystallization Process Image Analysis2Matini-Net: Versatile Material Informatics Research Framework for Feature Engineering and Deep Neural Network Design1Data-Quality-Navigated Machine Learning Strategy with Chemical Intuition to Improve Generalization13Expert-assisted statistical learning techniques for assessing wetland conditions in urban landscapes1Data-Efficient Multifidelity Training for High-Fidelity Machine Learning Interatomic Potentials87COBRA web application to benchmark linear regression models for catalyst optimization with few-entry datasets16Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives92Are We Overfitting to the Materials Project? A Critique of Dataset Bias in Property Prediction Benchmarks0Deep Learning-Based Effective Anomaly Detection in PWR-Type Nuclear Power Plants7Downscaling Photosynthetically Active Radiation Based on Two-Step Conversions With Consideration of Vegetation Forms1Identifying Co2 Flooding Field Applications for Carbon Sequestration Through Statistical Analysis and Machine Learning Methods0Learning Under Scarcity: A Conceptual Theory of Small-Data Regimes in Materials Artificial Intelligence0Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty21Probing out-of-distribution generalization in machine learning for materials81Simultaneous prediction of 10m and 100m wind speeds using a model inspired by brain emotional learning9Optimizing casting process using a combination of small data machine learning and phase-field simulations70Exploring Insecticidal Molecules with Random Forest: Toward High Insecticidal Activity and Low Bee 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Histogram Gradient Boosting, and Advanced Feature Selection for Predicting the Onset of Cardiovascular Diseases13Hybrid deep learning optimization for smart agriculture: Dipper throated optimization and polar rose search applied to water quality prediction7Artificial Intelligence for Materials Discovery, Development, and Optimization171Advanced Experiment Design Strategies for Drug Development3Artificial intelligence for post-moore field-effect transistors: a review and perspective1Hard Sample Mining: A New Paradigm of Efficient and Robust Model Training7Enhancing Reverse Design Ability of Functional Materials Based on Data Quality Management: Taking Biomedical Zinc Alloy as an Example0A comprehensive benchmark of active learning strategies with AutoML for small-sample regression in materials science12Multifunctional polyimide performance prediction based on explainable machine learning3A Novel Application of Choquet Integral for Multi-Model Fusion in Urban PM10 Forecasting1Impact of 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specimen to train path-dependent deep learning material models from a single uniaxial test: Eliciting strain diversity via automatically differentiable elastoplastic topology optimization0In context learning foundation models for materials property prediction with small datasets1Structural and Electronic Features-Integrated Machine Learning Framework for High-Throughput Prediction of Organic Pollutant Reactivity2A method for function name recovery in binaries via hierarchical semantic propagation0Seasonality and aerosol microenvironment-associated processing of organic aerosol in a humid inland city0Machine Learning for Superconductor Discovery: From Data-Driven Insights to Accelerated Design1A Comparative Study of Deep Learning Architectures for Anomaly Detection in PWRs: Achieving Optimal Accuracy, Efficiency, and Interpretability with a Feature-Engineered Multi-layer Perceptron1The Open Materials 2024 (OMat24) inorganic materials dataset and models20Dataset distillation for machine learning force field in phase transition regime0Machine learning-aided material selection, parameter optimization, and process monitoring in laser powder bed fusion and laser directed energy deposition with powder feed0Knowledge-informed data-efficient characterisation of soil spatial variability0Predicting drug release from polymeric long-acting injectables using a machine learning approach to decode formulation-performance relationships1A deep learning framework for inverse design of non-unique photonic structures: A DBSCAN-based quantitative analysis method0Studying Heat Conduction Properties by Using Machine Learning Potentials: a Brief Review0Data as the Backbone of Artificial Intelligence: Insights from Heterogeneous Catalysis0Deep learning for predicting partition-level homogenized properties for printed circuit board thermo-mechanical simulation: Edge-density-guided training-set design and cyclic training0From sample value to metallurgical roles: Constructing compact core sets 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  47. Probing out-of-distribution generalization in machine learning for materials

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