Research data supporting "Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials".

Reproducibility archive for validation-first machine learning interatomic potentials for local lithium energetics in graphene-based battery motifs.

Yuhan Sun, Xiao Huang

Chemistry-stratified reliability audit of selective abstention for universal machine-learning interatomic potentials

Zeyu Fu

Version 0.3.0 provides the sanitized, hermetic code and frozen result records supporting the chemistry-stratified selective-abstention analysis. It includes the 32-UIP roster analysis, prototype-blocked confidence intervals, mechanism and sensitivity controls, manuscript sources,…Read more

# Artificial Intelligence-Enabled Quantification of Cube and Goss Textures in Polycrystalline Materials: A Comprehensive Review of Machine Learning, Deep Learning, and EBSD-Based Characterization Approaches

Sudhakar Geruganti

## ALTERNATIVE TITLES ### Alternative Title 1 (Comprehensive)**"AI-Driven Analysis of Cube {100}<001> and Goss {110}<001> Textures: Machine Learning, Deep Learning, and Generative Models for Crystallographic Texture Quantification in Metallurgical Engineering"** ### Alternative T…Read more

Simple Data Cleaning Techniques for Improving Machine Learning

Jyoti Panthangi

High-quality data is essential for building reliable machine learning models. Raw datasets often contain missing values, outliers, duplicates, inconsistent formats, and unstructured categorical variables. These issues reduce model accuracy and lead to biased predictions. This pap…Read more

RAQA-AutoML: An Integrated Solution for Automated Machine Learning

Abdullah Kaviani Rad

Overview AutoML-Lite is a powerful, user-friendly desktop application designed to democratize machine learning by automating the entire modeling pipeline. Built with Python and PyQt6, it provides a comprehensive GUI-based environment for data preprocessing, feature engineering, m…Read more

Research data and code supporting "Label-free biochemical imaging and time point analysis of neural organoids via deep learning–enhanced Raman microspectroscopy"

Dimitar Georgiev, Ruoxiao Xie, Daniel Reumann, X Zhao, A. Fernandez-Galiana, Mauricio Barahona, et al.

This repository contains the datasets and source code associated with Georgiev et al., Science Advances (2026). https://doi.org/10.1126/sciadv.aec5080 To get started with the software, visit our GitHub repository. The software provides both a graphical user interface (GUI) and a…Read more

Also available via: European Organization for Nuclear Research