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RESEARCH PAPER · Communications Materials · 2025

Probing out-of-distribution generalization in machine learning for materials

Kangming Li, Andre Niyongabo Rubungo, Xiangyun Lei, Daniel Persaud, Kamal Kumar Choudhary, Brian DeCost, Adji Bousso Dieng, Jason R. Hattrick-Simpers

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Probing out-of-distribution generalization in machine learning for materialsOriginalCompositional Generalization as a Distinct Failure Mode in Materials AI0Machine and deep learning performance in out-of-distribution regressions6Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility17MatFold: systematic insights into materials discovery models' performance through standardized cross-validation protocols17AI-powered exploration of molecular vibrations, phonons, and spectroscopy19An AI-accelerated pathway for reproducible and stable halide perovskites17Beyond training data: how elemental features enhance ML-based formation energy predictions0Coherent collections of rules describing exceptional materials identified with a multi-objective optimization of subgroups2Large language models for material property predictions: elastic constant tensor prediction and materials design14Out-of-Distribution Generalization in Materials AI: A Systematic Review of Domain Shift, Robustness, and What Remains Unsolved0Machine-learning-potential-driven prediction of high-entropy ceramics with ultra-high melting points15Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models28Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models1A general approach for determining applicability domain of machine learning models56LLM4Mat-bench: benchmarking large language models for materials property prediction24Revealing the interstitial-mediated sluggish diffusion mechanism in concentrated solid-solution alloys via machine learning-integrated kinetic Monte Carlo0Evaluating GPT-4’s Semantic Understanding of Obstetric-based Healthcare Text through Nurse Ruth1Media Polarity Control Strategy to Tailor Mechanical Behavior of Dual Monomer Single Network Hydrogels and Integrated Machine Learning Approach1Stochastic deep learning surrogate models for uncertainty propagation in microstructure–properties of ceramic aerogels5Machine learning interatomic potentials in biomolecular modeling: principles, architectures, and applications7The importance of negative training data for robust antibody binding prediction3Synergy of Machine Learning and High‐Throughput Experimentation: A Road Toward Autonomous Synthesis8QC-Augmented NN/GNN for Few-Shot Prediction of Amorphous Polymer Properties Via Multi-Scale Microstructures1Identify Survived Key Features and Relevant Mechanisms for Designing High-Entropy Carbides via AI or Machine Learning2Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments10A Computational Renaissance in High-Energy Density Materials (HEDMs) Research38Data Driven Band Gap Prediction of Oxides for Sustainable Energy Applications0AI‐Assisted Design of Advanced Polymeric Materials: Challenges and Solutions25Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules29Accelerating multimetallic catalyst discovery with robotics and agentic AI8Real-space machine learning of correlation density functionals13Understanding and mitigating distribution shifts for universal machine learning interatomic potentials4Accelerating catalytic advancements through the precision of high-throughput experiments & calculations4Dynamic Metrics to Validate the Use of Machine-Learned Interatomic Potential for Dynamic Sampling0AI-powered open-source infrastructure for accelerating materials discovery and advanced manufacturing27Robust learning from literature data: Model generalizability and uncertainty for predicting conjugated polymer solution conformation0Achieving exceptional strength-ductility synergy in transition metal high-entropy alloys via machine learning-assisted multi-objective optimization0Out-of-distribution machine learning for materials discovery: Challenges and opportunities5A Technical Perspective to Human Oversight in Complex Drilling Automation Systems Based on Artificial Intelligence Methods0Generative Active Learning Discovers High-Performance O2 Reduction Catalysts for H2O2 Production2Motor temperature signal reconstruction and wide-temperature-range identification based on a physics-constrained neural network3Overcoming Roadblocks for Implementing AI/ML Methods for Materials Advancement3Sustainable Materials Design With Multi‐Modal Artificial Intelligence1In context learning foundation models for materials property prediction with small datasets1A Review of Knowledge Base Construction Strategies for LLM-based Intelligent Decision Support System in Material Selection and Process Planning0Chemical-transferability limits of HEA phase selection: Element-exclusion validation and descriptor-physics effects1Fast and Accurate Prediction of Solid–Liquid Equilibria in Type V Eutectic Systems Using openCOSMO-RS1Real-time 3D thermal prediction for advanced packaging via machine learning2D2VP: A Visual First Platform for Diagnosing Data Distribution Mismatch Toward Reliable Machine Learning in Materials Science2Integrated PredRNN++ and phase-field for microstructure prediction and simulation acceleration1Machine Learning-Driven Prediction and Mechanistic Insight into CO 2 Adsorption on Biomass-Derived Activated Carbons Using Explainable AI (XAI)1Application and challenges of artificial intelligence technology in ODS steel research: A review0Explainable and comparative machine learning models for punching shear prediction in reinforced concrete flat slabs3Physics-informed residual learning with low-rank adaptation for unsupervised mesh generation0Charge–Capacitance Channel Decomposition Reveals Fabrication-Tolerant Design Windows for Disk Triboelectric Nanogenerators0Intelligent Digital Rock Physics: Advances and Perspectives from Imaging Reconstruction to Pore-Scale Multiphase Flow Simulation3Retrosynthetic Network Assessment (ReNA): Automative Retrosynthesis Opens Pathways To Integrate Life Cycle Thinking into Green Chemistry Metrics0Noise augmented machine learning for predicting ageing hardening behavior of Mg–Gd alloys: From data scarcity to physically interpretable modelling0From Chatbots to Chemists: Autonomous Agents for Heterogeneous Catalysis0Cross-laboratory generalization failure in perovskite solar cell machine learning: A diagnostic protocol and evaluation threshold1From processing text to counterfactual steel design: a knowledge-graph-organized and reliability-aware machine learning framework0DuetDA: Decomposed and Dynamic Data Attribution with Model-State Gating for Accelerated Scientific Endeavors0Data as the Backbone of Artificial Intelligence: Insights from Heterogeneous Catalysis0Δ‐Machine Learning for the Prediction of Metal Complex Properties0Feature engineering improves cross-mode generalization in machine learning-based prediction of membrane capacitive deionization1A controlled framework for chemistry-shift evaluation in descriptor-based materials screening0From natural-extract variability to reproducible nanomaterials: AI-QbD strategies for green synthesis from agro-food waste0From Prediction to Physical Meaning in Materials AI0From Single Omics to Digital Design: The Evolution of Food Flavor Science and Associated Challenges and R&D Opportunities0Machine learning for lignocellulosic pretreatment and lignin valorization: a biomass-centered framework from process conditions to molecular design0A comprehensive review of thermoelectric power generators: Advances in material discovery, structural optimization for performance enhancement, and service life stability0A physics-corrected ML/DL framework for heat flux field prediction in porous structure of sintered Ag2Machine learning guided high-throughput screening of multi-element-doped perovskites for efficient photothermal CO2 reduction to methanol0Finite element–machine learning surrogates for synthetic recovery of breast tissue elastic properties: Observation sensitivity and generalization analysis0Machine Learning‐Driven Rational Design of Organic Electro‐Oxidation Catalysts: A Mechanism‐Embedded Approach to Selectivity Engineering0Trustworthy artificial intelligence in advanced manufacturing0From fidelity to modelability: Advancing electronic band gap prediction of materials with AI diagnostics0Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering0Avaliação Heurística da Operação da Eletrólise Alcalina Pressurizada da Água Utilizando um Sistema de Inferência Fuzzy Mamdani0The necessity of physics-informed neural networks for adsorption-based water treatment machine learning models: a case study of biochar adsorption0A parametric physics-informed deep learning approach for 6th-order bending analysis of MSGT microbeams0

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