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RESEARCH PAPER · Communications Materials · 2022

Why big data and compute are not necessarily the path to big materials science

Naohiro Fujinuma, Brian DeCost, Jason R. Hattrick-Simpers, S. E. Lofland

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Why big data and compute are not necessarily the path to big materials scienceOriginalConceptual Foundations for Adversarial Validation in Materials Machine Learning0Scientific Overconfidence in High-Performing Materials AI Systems0A domain knowledge enhanced machine learning method to predict the properties of halide double perovskite A 2 B + B 3+ X 615Algorithmic Novelty vs Chemical Novelty: Rethinking Innovation Metrics0Benchmarking Without Reality: Dataset Construction Bias in Materials Evaluation0Computational and Data-Driven Materials Engineering: Multimodal Materials Datasets, Integration Frameworks, and Discovery Potential0Failure Case Reporting in Materials Informatics — What Is Documented and What Is Silenced0Machine learning using multi-modal data predicts the production of selective laser sintered 3D printed drug products72Probe microscopy is all you need *20Carbon Footprint of Artificial Intelligence in Materials Science: Should We Be Concerned?1Carbon Footprint of Artificial Intelligence in Materials Science: Should We Be Concerned?0Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations32In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99AI-enabled materials discovery for advanced ceramic electrochemical cells28Intelligent processing and development of high-performance automotive aluminum alloys: Application of physics-based and data-driven modeling1Navigation through high-dimensional chemical space: discovery of Ba 5 Y 13 [SiO 4 ] 8 O 8.5 and Ba 3 Y 2 [Si 2 O 7 ] 22Discovering Electrochemistry with an Electrochemistry‐Informed Neural Network (ECINN)17Discovering Electrochemistry with an Electrochemistry‐Informed Neural Network (ECINN)3Accelerating materials language processing with large language models78Revisiting El-Sayed Synthesis: Bayesian Optimization for Revealing New Insights during the Growth of Gold Nanorods12The carbon footprint of predicting CO2 storage capacity in metal-organic frameworks within neural networks8Ontology-Oriented Modeling of the Vickers Hardness Knowledge Graph5Prevention of Leakage in Machine Learning Prediction for Polymer Composite Properties16Machine learning for efficient grazing-exit x-ray absorption near edge structure spectroscopy analysis: Bayesian optimization approach4Key requirements for advancing machine learning approaches in single entity electrochemistry10CSIML: a cost-sensitive and iterative machine-learning method for small and imbalanced materials data sets5A Vision for the Future of Materials Innovation and How to Fast-Track It with Services2Machine Learning Algorithm Guides Catalyst Choices for Magnesium‐Catalyzed Asymmetric Reactions7Machine Learning Algorithm Guides Catalyst Choices for Magnesium‐Catalyzed Asymmetric Reactions32Machine learning-enhanced optimal catalyst selection for water-gas shift reaction7Accelerating Materials Discovery for Polymer Solar Cells: Data-Driven Insights Enabled by Natural Language Processing10An ontology-based text mining dataset for extraction of process-structure-property entities15Classification-Based Detection and Quantification of Cross-Domain Data Bias in Materials Discovery9Mapping the structure-function landscape of semiconducting polymers10The Problem of Representational Harm in Materials Dataset Construction0Machine learning strategies for small sample size in materials science41A CRITICAL REVIEW OF DEEP LEARNING APPLICATIONS, CHALLENGES, AND FUTURE DIRECTIONS IN STRUCTURAL ENGINEERING3Mind the gap: Bridging the divide between AI aspirations and the reality of autonomous microscopy6Unlocking a self-driving research workflow for perovskite photovoltaics12Flow-driven data intensification to accelerate autonomous inorganic materials discovery25Lessons from the trenches on evaluating machine learning systems in materials science10Beyond batteries: Architecting materials through surface chemistry and engineering for revolutionizing hydrogen storage3General-Purpose Models for the Chemical Sciences: LLMs and Beyond6Small features, big impact: Influence of overlooked microstructural features on high-entropy alloy predictions5Federated Learning for Privacy-Preserving Data Science: Performance, Efficiency, and Scalability Analysis0CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning1Biophotoelectrochemical systems for solar-driven nitrogen conversion: Interfacial design and redox coupling across the nitrogen cycle0Machine learning for titanium alloy development: Composition design, process optimization, microstructure informatics, and performance prediction0Machine learning for biomaterials design2From natural-extract variability to reproducible nanomaterials: AI-QbD strategies for green synthesis from agro-food waste0Language-model-guided pre-screening of porous carbon electrodes: Overcoming data fragmentation for reliable capacitance prediction0

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