ReviewChemical science2023
Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry.
Review in Chemical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- The Applications of Machine Learning in Micro-Nano Materials Research: From High-Throughput Screening to Intelligent Design.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Probing Buried Interfaces in Batteries: TowardChemistry of materials : a publication of the American Chemical Society · 2026Review
- A Conversational Large-Language-Model Tutor that Accelerates Machine-Learning Method Development in Routine Bioanalytical Workflows.Chembiochem : a European journal of chemical biology · 2025Article
- Review
- Machine-learning-informed scattering correlation analysis of sheared colloids.Journal of applied crystallography · 2025Article
- Machine Learning Interpretation of Optical Spectroscopy Using Peak-Sensitive Logistic Regression.ACS nano · 2025Article
- Surface-Enhanced Raman Spectroscopy for Biomedical Applications: Recent Advances and Future Challenges.ACS applied materials & interfaces · 2025Review
- Nonlinear Harmonics: A Gateway to Enhanced Image Contrast and Material Discrimination.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Machine learning and data-driven methods in computational surface and interface science.npj computational materials · 2025Review
- A Vision for the Future of Neutron Scattering and Muon Spectroscopy in the 2050s.ACS physical chemistry Au · 2024Review
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid growth of materials chemistry data, driven by advancements in large-scale radiation facilities as well as laboratory instruments, has outpaced conventional data analysis and modelling methods, which can require enormous manual effort. To address this bottleneck, we investigate the application of supervised and unsupervised machine learning (ML) techniques for scattering and spectroscopy data analysis in materials chemistry research. Our perspective focuses on ML applications in powder diffraction (PD), pair distribution function (PDF), small-angle scattering (SAS), inelastic neutron scattering (INS), and X-ray absorption spectroscopy (XAS) data, but the lessons that we learn are generally applicable across materials chemistry. We review the ability of ML to identify physical and structural models and extract information efficiently and accurately from experimental data. Furthermore, we discuss the challenges associated with supervised ML and highlight how unsupervised ML can mitigate these limitations, thus enhancing experimental materials chemistry data analysis. Our perspective emphasises the transformative potential of ML in materials chemistry characterisation and identifies promising directions for future applications. The perspective aims to guide newcomers to ML-based experimental data analysis.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.