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RESEARCH PAPER · npj Computational Materials · 2024

Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study

Sadman Sadeed Omee, Nihang Fu, Rongzhi Dong, Ming Hu, Jianjun Hu

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Structure-based out-of-distribution (OOD) materials property prediction: a benchmark studyOriginalA reproducibility study of atomistic line graph neural networks for materials property prediction5Comparative Analysis of Conventional Machine Learning and Graph Neural Network Models for Perovskite Property Prediction16Setting standards for data driven materials science24MD-HIT: Machine learning for material property prediction with dataset redundancy control53Generative AI-Powered Inverse Design for TailoredNarrowband Molecular Emitters0Machine and deep learning performance in out-of-distribution regressions6MatFold: systematic insights into materials discovery models' performance through standardized cross-validation protocols17Physical encoding improves OOD performance in deep learning materials property prediction9Optimal pre-train/fine-tune strategies for accurate material property predictions19A Theory of Justified Abstention for Uncertain Materials AI Models0AI-powered exploration of molecular vibrations, phonons, and spectroscopy19Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials4Generative AI-powered inverse design for tailored narrowband molecular emitters4High-throughput screening of stable Ag–Pd–F catalysts for formate oxidation reaction using machine learning10Machine learning-driven elasticity prediction in advanced inorganic materials via convolutional neural networks1Out-of-Distribution Generalization in Materials AI: A Systematic Review of Domain Shift, Robustness, and What Remains Unsolved0Position: Federated Learning for Proprietary Materials Databases — Privacy-Preserving Training without Centralized Data0Probing out-of-distribution generalization in machine learning for materials81AutoMTNAS: Automated meta-reinforcement learning on graph tokenization for graph neural architecture search7PINK: physical-informed machine learning for lattice thermal conductivity8Out-of-Distribution Material Property Prediction Using Adversarial Learning6AI-driven materials design: paradigm shift from small data to big data1Designing laser powder bed fusion low-alloyed titanium with superior strength-ductility trade-off via machine learning15Assessing Uncertainty in Machine Learning for Polymer Property Prediction: A Benchmark Study23A critical review of machine learning interatomic potentials and Hamiltonian14Artificial Intelligence for Materials Discovery, Development, and Optimization171Foundation model for composite microstructures: Reconstruction, stiffness, and nonlinear behavior prediction8Prediction and characterization of chemically complex σ − phase intermetallics with graph neural network3Evaluating the use of a machine learning simulator for structure–property prediction: A case study on disordered elastic networks2A survey of AI-supported materials informatics7Genarris 3.0: Generating Close-Packed Molecular Crystal Structures with Rigid Press7Machine learning-enabled multiscale modeling of mechanical deformation of aluminum and Al-SiC nanocomposites5Genetic algorithm-based inverse design of symmetry-broken re-entrant honeycomb13AI‐Assisted Design of Advanced Polymeric Materials: Challenges and Solutions25Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules29Deep learning-assisted prediction of electric field impact on impurity photoionization cross section in CdS/ZnSe core/shell spherical quantum dot embedded in PVA matrix1SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction6From microstructure to performance optimization: Innovative applications of computer vision in materials science13SSFormer: Self-supervised transformer model for predicting the properties of crystalline materials1High-performance interpretable model for exfoliation energy prediction of 2D nanomaterials0Continuous discovery of novel 2D materials via dual active learning-driven generative models3Future-oriented precision creation: recent advances in the intelligent design and synthesis of fine chemicals1Symbolic regression for defect interactions in 2D materials1Out-of-distribution machine learning for materials discovery: Challenges and opportunities5Li–P–S Electrolyte Materials as a Benchmark for Machine-Learned Interatomic Potentials7In context learning foundation models for materials property prediction with small datasets1Chemical-transferability limits of HEA phase selection: Element-exclusion validation and descriptor-physics effects1Fractal Multiscale Geometric Learning for Materials Property Prediction via Cross-Attention Fusion0AI-enabled cross-scale modeling and parameter transfer for lithium-ion batteries1BiL-AttNet: A deep learning architecture for structure-agnostic material property prediction0Artificial intelligence and the frontier of phonon engineering: a perspective on discovering extreme thermal materials0From processing text to counterfactual steel design: a knowledge-graph-organized and reliability-aware machine learning framework0Using AI in Food Hydrocolloid Research: Evidence Quality, Mechanistic Explainability, and Real-Food Validation0A controlled framework for chemistry-shift evaluation in descriptor-based materials screening0Mining and Metallurgical Waste Valorization for Water Treatment: A Review0Data- and AI-Driven Catalysis Science: From Descriptors to Digital Catalysis for Next-Generation Catalysts0From Prediction to Physical Meaning in Materials AI0Machine learning-guided design of rare-earth single-atom catalysts for the oxygen reduction reaction0A PSO-Optimized stacked ensemble learning approach for accurate melanoma detection with deep transfer learning and explainability0Artificial Intelligence in Scalable Materials Synthesis and Manufacturing0Machine learning for lignocellulosic pretreatment and lignin valorization: a biomass-centered framework from process conditions to molecular design0AI-driven property prediction and design optimisation of composite materials: A review0Dynamic evaluation of uncertainty quantification under distribution shift in materials property prediction0Reliability-bounded composition-only learning of calculated magnetic moments in Fe-rich materials0Shift-Robust Quality Prediction in Sugar Crystallization Using Physics-Guided Gradient Boosting0Machine Learning of Band Gaps and Thermodynamic Stability in A2BB′X6 (X = F, Cl, Br, I) Double Perovskites with Uncertainty-Aware Multi-Objective Screening0

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