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A Vision for the Future of Neutron Scattering and Muon Spectroscopy in the 2050s

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Nonlinear Harmonics: A Gateway to Enhanced Image Contrast and Material Discrimination

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Surface-Enhanced Raman Spectroscopy for Biomedical Applications: Recent Advances and Future Challenges

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Machine Learning-Based Material Thickness and Type Classification via Scattering Parameters

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Machine Learning Interpretation of Optical Spectroscopy Using Peak-Sensitive Logistic Regression

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Gaining More Insights from Synchrotron-Based X-ray Spectroscopy for Alkali Ion Rechargeable Batteries

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Bonding Mechanisms Underpinning Structural and Electronic Properties of Halide Perovskites

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Revealing the hidden structural complexity of high-capacity oxide cathodes through advanced characterizations

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Artificial intelligence transforming materials research: possible road ahead for India

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The decontaminant mechanism of polyamide membranes for sulfamethoxazole: The insights from combined machine learning and molecular modelling

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Predicting Materials Thermodynamics Enabled by Large Language Model‐Driven Dataset Building and Machine Learning

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A Conversational Large‐Language‐Model Tutor that Accelerates Machine‐Learning Method Development in Routine Bioanalytical Workflows

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Machine learning and data-driven methods in computational surface and interface science

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Uncovering obscured phonon dynamics from powder inelastic neutron scattering using machine learning

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Recent Progress of Artificial Intelligence Application in Polymer Materials

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Quantifying dispersity in size and shape of nanoparticles from small-angle scattering data using machine learning based CREASE

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POMFinder: identifying polyoxometallate cluster structures from pair distribution function data using explainable machine learning

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Probing Buried Interfaces in Batteries: Toward Operando Visibility and Quantitative Diagnosis

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Exploring the limitations of kNN noisy feature detection and recovery for self-driving labs

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Researching the phenomenon of collision electrification between particles and flat materials based on machine learning

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Machine-learning-informed scattering correlation analysis of sheared colloids

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Hepatic toxicity prediction of bisphenol analogs by machine learning strategy

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Predicting Colloidal Interaction Parameters from Small-Angle X-ray Scattering Curves Using Artificial Neural Networks and Markov Chain Monte Carlo Sampling

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Rapid detection of microbial antibiotic susceptibility via deep learning supported analysis of angle-resolved scattered-light images of picoliter droplet cultivations

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Computational Reverse Engineering Analysis of the Scattering Experiment Method for Interpretation of 2D Small-Angle Scattering Profiles (CREASE-2D)

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Machine learning for analyses and automation of structural characterization of polymer materials

Shizhao Lu and Arthi Jayaraman

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