Companion dataset and pipeline: microstructure-aware machine learning prediction of hydrogen embrittlement susceptibility in steels

Dataset for Hybrid Machine Learning Framework for Microstructure-Based Composition Reconstruction and Hardness Prediction of Al–Si Die-Casting Alloys

Uro Heo, Taehyun Kim, Youngje Kwon, Jingyu Seo, K.H. Kim, Namhyun Kang

This dataset contains the data used in the paper "Hybrid Machine Learning Framework for Microstructure-Based Composition Reconstruction and Hardness Prediction of Al–Si Die-Casting Alloys". The dataset (approximately 3GB) is divided into two main parts: OMtoEDS: Contains the data…Read more

Before the Model: Why Datasets and Data Representation Define What Machine Learning Can Learn

Jean Franck Loa Rojas

Machine learning systems do not learn reality directly; they learn from the representations preserved in their datasets. This structured narrative review examines how dataset purpose, coverage, integrity, labeling, independence, reproducibility, governance, and continuity determi…Read more

A Machine-Learning-Driven Dataset of 140,000 PAH Infrared Spectra

Xinghong Mai

This ZIP archive contains the infrared (IR) spectral dataset of polycyclic aromatic hydrocarbons (PAHs) presented in the companion paper. The dataset comprises 144,111 IR spectra across 48,037 closed-shell, even-carbon benzenoid PAH structures in neutral, cationic, and anionic ch…Read more

# Artificial Intelligence in Metallurgical Engineering: A Comprehensive Review of Applications, Challenges, and Future Direction

Sudhakar Geruganti

## ALTERNATIVE TITLES ### Alternative Title 1 (Comprehensive)**"AI-Driven Transformation in Metallurgical Engineering: From Microstructure Analysis to Smart Manufacturing and Sustainable Production"** ### Alternative Title 2 (Process-Focused)**"Machine Learning and Deep Learning…Read more

Reproducibility package for: Validation-Induced Optimism in Machine-Learning Prediction of Concrete Compressive Strength: An Open-Access Methodological Audit and Leakage-Controlled Multi-Dataset Benchmark

Prasanna Jibi Ghimire, Prerana Khatiwada

A causal perspective on Machine Learning for concrete quality predictions and data-driven mixture optimization

Thorsten Kalb, Anil Esen, Elsa Qoku, Thomas Matschei, Chiara Masiero, Gian Antonio Susto

Machine Learning (ML) predictions of cement and concrete quality and subsequent data-driven mixture optimization has been advertised for almost three decades. However, supervised ML leverages correlations, not causal relationships. Aiming for hybrid models, we derive the first ca…Read more