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RESEARCH PAPER · npj Computational Materials · 2023

A critical examination of robustness and generalizability of machine learning prediction of materials properties

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

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A critical examination of robustness and generalizability of machine learning prediction of materials propertiesOriginalFailure, Uncertainty, and Risk in Materials AI — How Negative Outcomes Are Handled Across the Literature: A Review Study0Scientific Overconfidence in High-Performing Materials AI Systems0A Conceptual Distinction between Generalization and Transfer in Materials Machine Learning0Algorithmic 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 Potential0Data-Driven Materials Engineering: Inverse Design Strategies, Machine Learning Architectures, and Application Domains0Discovery Pipelines as Epistemic Filters: What Computational Workflows Exclude0Scientific Blind Spots Introduced by Feature Engineering in Materials Informatics0Uranium and lithium extraction from seawater: challenges and opportunities for a sustainable energy future125ET-AL: Entropy-targeted active learning for bias mitigation in materials data30An interpretable LSTM deep learning model predicts the time-dependent swelling behavior in CERCER composite fuels13Data-Driven Prediction of Configurational Stability of Molecule-Adsorbed Heterogeneous Catalysts2In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99Building materials genome from ground‐state configuration to engineering advance2Exploiting redundancy in large materials datasets for efficient machine learning with less data118Multi-principal element alloy discovery using directed energy deposition and machine learning35A call for caution in the era of AI-accelerated materials science2Systems Analysis of Bias and Risk in AI-Enabled Medical Diagnosis10A Universal Validation Method for Mitigating Machine Learning Extrapolation Risk1Realistic material property prediction using domain adaptation based machine learning48A Conceptual Theory of Measurement Validity for AI-Generated Materials Properties1Algorithmic Consensus vs Scientific Consensus in Materials Prediction0Efficient first principles based modeling via machine learning: from simple representations to high entropy materials23Extrapolation validation (EV): a universal validation method for mitigating machine learning extrapolation risk28Representation Drift in Iterative Materials Learning Systems0Unsupervised learning and pattern recognition in alloy design15Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning70Generalization Across Experimental Parameters in Neural Network Analysis of High-Resolution Transmission Electron Microscopy Datasets14Efficient Modeling of Composition-Dependent Lattice Constants of Multicomponent Metallic Solid Solutions Based on High-Throughput Experiment0Feature purify: An examination of spurious correlations in high-entropy alloys9Prevention of Leakage in Machine Learning Prediction for Polymer Composite Properties16Modulating thermal and electrical conductivities in polymers: An approach toward extracting molecular design rules through atomistic simulations7Quantitative predictive theories through integrating quantum, statistical, equilibrium, and nonequilibrium thermodynamics29JARVIS-Leaderboard: a large scale benchmark of materials design methods65Unveiling yield strength of metallic materials using physics-enhanced machine learning under diverse experimental conditions35A Study Towards Machine Learning Prediction of Thermal Conductivity of Polymers Based on Molecular Dynamics0Designing unique and high-performance Al alloys via machine learning: Mitigating data bias through active learning24Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study66Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials’ Surfaces116Evaluating external generalizability of machine learning models for recycled aggregate concrete property prediction14The Impact of Domain Knowledge on Universal Machine Learning Models8Optimization and prediction of dye adsorption utilising cross-linked chitosan-activated charcoal: Response Surface Methodology and machine learning48Machine learning in materials research: Developments over the last decade and challenges for the future105MD-HIT: Machine learning for material property prediction with dataset redundancy control53Graph neural network guided design of novel deep-ultraviolet optical materials with high birefringence23Machine Learning in 3D and 4D Printing of Polymer Composites: A Review48Causality-Driven Feature Selection for Calibrating Low-Cost Airborne Particulate Sensors Using Machine Learning4Impact of data bias on machine learning for crystal compound synthesizability predictions16Bayesian assessment of commonly used equivalent circuit models for 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energy conversion0Multi-Objective density optimization of Cu-Cu bonded stacked dies under thermal cycling via desirability function analysis0From fidelity to modelability: Advancing electronic band gap prediction of materials with AI diagnostics0Artificial Intelligence as a Disruptive Tool in Interdisciplinary Research: Current Trends and Emerging Applications0Recent advances in carbon materials for energy storage applications: a comprehensive review of supercapacitors and lithium-ion batteries0Deciphering composition-property relationships in alkali activated binders for design via an interpretable Bayesian optimized stacking framework0

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