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RESEARCH PAPER · npj Computational Materials · 2024

The mastery of details in the workflow of materials machine learning

Yingying Ma, Pengcheng Xu, Minjie Li, Xiaobo Ji, Wenyue Zhao, Wencong Lu

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The mastery of details in the workflow of materials machine learningOriginalScientific Overconfidence in High-Performing Materials AI Systems0Scientific Blind Spots Introduced by Feature Engineering in Materials Informatics0The Scaffolding Problem: How Materials AI Depends on Unstated Scientific Infrastructure0Imputation of Missing Data in Materials Science through Nearest Neighbors and Iterative Predictions16Out-of-Distribution Generalization in Materials AI: A Systematic Review of Domain Shift, Robustness, and What Remains Unsolved0Wet chemistry nanoarchitectonics for nanocrystal-based films3Heat Transfer in Composite Materials: Mechanisms and Applications8EFTGAN: Elemental features and transferring corrected data augmentation for the study of high-entropy alloys20Computational insights into PAN-based carbon fiber advancements: Simulation techniques, microstructural-property relationship, and future directions17Accelerating tribological material discovery: A machine learning framework with predictive insights20Machine learning perspective: Revealing deep mechanisms and new advances in adsorption and catalysis of gaseous molecules18Generalizable descriptors for automatic titanium alloys design by learning from texts via large language model21Machine learning-driven insights into the microstructure and properties of high-entropy alloys14Materials informatics: A review of AI and machine learning tools, platforms, data repositories, and applications to architectured porous materials41Revolutionizing Chemical Mechanical Polishing with Machine Learning: A Comprehensive Review of Challenges and Opportunities5High-Performance Polyimide Design: Machine Learning–Accelerated Multiobjective Optimization6Artificial intelligence for post-moore field-effect transistors: a review and perspective1Elucidating the key drivers of twin formation in copper alloys via a small-sample AutoML framework0Prediction of Key Properties in Multiple Resonance Thermally Activated Delayed Fluorescence Materials Using Lightweight Feature Encoding6Creating Gallium-based alloys with the lowest melting point via reverse design approach4Data-Driven Modeling and Optimization of Heat Treatment Parameters for Aluminum Alloys Using Advanced Machine Learning4Data-driven and computational insights into borophene: Integrating machine learning with DFT and AI10Machine Learning Unlocks New Directions in Halide Perovskite Research6Based on machine learning prediction and experimental verification: The optimization of NH3-SCR performance on Ce/Co-modified MnNi catalysts8Automatic hydrogen clathrate recognition neural networks for high-pressure superconductor prediction1Identification of key descriptors for C–C coupling on dual-atom catalysts0Machine Learning’s Emergence in Predictive and Prescriptive Materials Design Modeling0Modern technology application in ethnoscience: machine learning, digital tools, and sustainability0Role of Machine Learning in the Perovskite Solar Cell Investigations0Small-Data and Sparse-Regime Learning in Materials AI — Methods, Assumptions, and Limits0Two-stage interpretable machine learning for hydrometallurgical recycling of spent LiFePO 4 batteries: integrating pretreatment and leaching for process optimization0Research progress on liquid-solid transition under synchrotron radiation X-ray and simulation1Machine Learning in Ultra-High-Performance Concrete (UHPC): A Multiscale Review of Applications, Challenges, and Prospects2Sample-efficient active learning for materials informatics using integrated posterior variance5Deep learning-assisted efficient design of biomimetic fluorescent dielectric elastomers0The Use of Machine Learning in Aluminum and Magnesium Matrix Composites: A Review3AI-driven multi-property prediction and manufacturability-aware design for electronic materials and devices0From green routes to digital design: Two-dimensional photocatalysts at the interface of machine learning and environmental applications0Comparative analysis of artificial neural network and benchmark models for predicting mechanical properties of recycled polypropylene/spent coffee grounds composites0Intelligent X-ray Spectroscopy through Machine Learning for Energy Materials Discovery and Characterization0Physics-informed transfer learning via frontier orbital pretraining for prediction of polymer electronic properties0SemiConLens: Visual Analytics for 2D Semiconductor Discovery0Intelligent Injection Molding: Machine Learning-Driven Optimization of Processing Parameters for Enhanced Mechanical Properties in Short-Fiber-Reinforced Thermoplastics0Multimodal adaptive machine learning to assist design of non-heat treatable high pressure die-casting Al-Si alloys0Noise augmented machine learning for predicting ageing hardening behavior of Mg–Gd alloys: From data scarcity to physically interpretable modelling0Data-driven optimization of particle size for porous polylactic acid microspheres: A Gaussian process regression-driven active learning approach0Fatigue Data Infrastructure for Reliability Analytics: A Relational Database Framework for Traceable Fatigue Knowledge Integration0Artificial intelligence in impurity prediction: current landscape, challenges, and future directions0Uncertainty-aware multi-source modeling for materials property prediction: A case study in ferroelectrics0Artificial intelligence for carbon materials in electrochemical energy storage: Methods, applications, and perspectives0Machine Learning Meets Photocatalytic Water Splitting for Sustainable Hydrogen Production0Mapping coupled composition-processing effects on the strength and ductility of magnesium alloys with interpretable machine learning0Machine Learning for Structural Steels: Materials Design, Property Prediction, Durability, and Future Directions0Mechanism-informed machine learning for intelligent design of rare-earth-containing magnesium-based alloys0Interpretable Tg prediction for polyurethane elastomers under restricted-distribution conditions0Machine learning-accelerated discovery and optimization of thermoelectric materials for energy conversion0FC-Transformer multiple features fusion for predicting porosity and ILSS in CFRP autoclave processing0Machine Learning of Band Gaps and Thermodynamic Stability in A2BB′X6 (X = F, Cl, Br, I) Double Perovskites with Uncertainty-Aware Multi-Objective Screening0Machine Learning‐Driven Rational Design of Organic Electro‐Oxidation Catalysts: A Mechanism‐Embedded Approach to Selectivity Engineering0Interpretable machine learning reveals morphology-mediated performance pathways in organic solar cells0Language-model-guided pre-screening of porous carbon electrodes: Overcoming data fragmentation for reliable capacitance prediction0Integrated classical/quantum–classical neural networks, DFT, and experiments for predicting bandgap and color of CrxSbxTi1−2xO2 yellow-orange pigments0Experimental investigation of multiaxial fatigue behavior and life prediction using Transformer-PINN in Q355E steel welded joints0Special Issue on Design, Development, and Characterization of Advanced Materials for Modern Industry0

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