ArticleNature communications2020
Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- Deep autoencoder for low dimensionality for high dimensional data in regression models and direct inverse analysis of models.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2026Article
- Infrared Spectra Prediction for a Carbonyl Group Utilizing a Graph Network Approach.Precision chemistry · 2026Article
- Machine learning spectroscopy to advance computation and analysis.Chemical science · 2025Review
- Developing machine learning for heterogeneous catalysis with experimental and computational data.Nature reviews. Chemistry · 2025Review
- Article
- Best practices for in-situ and operando techniques within electrocatalytic systems.Nature communications · 2025Review
- Monitoring C-C coupling in catalytic reactions via machine-learned infrared spectroscopy.National science review · 2025Article
- Efficient and accurate determination of the degree of substitution of cellulose acetate using ATR-FTIR spectroscopy and machine learning.Scientific reports · 2025Article
- Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction.npj computational materials · 2025Article
- Design Principles of Catalytic Materials for COAdvanced materials (Deerfield Beach, Fla.) · 2024Article
- Raman spectroscopic deep learning with signal aggregated representations for enhanced cell phenotype and signature identification.PNAS nexus · 2024Article
- AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring.Biosensors · 2024Review
- Improving sensitivity of XANES structural fit to the bridged metal-metal coordination.Journal of synchrotron radiation · 2024Article
- Regenerated Fiber's Ideal Target: Comparable to Natural Fiber.Materials (Basel, Switzerland) · 2024Review
- Spectroscopy-Guided Deep Learning Predicts Solid-Liquid Surface Adsorbate Properties in Unseen Solvents.Journal of the American Chemical Society · 2024Article
- Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling.iScience · 2023Review
- Artificial intelligence-coupled plasmonic infrared sensor for detection of structural protein biomarkers in neurodegenerative diseases.Science advances · 2023Article
- Machine learning-assisted crystal engineering of a zeolite.Nature communications · 2023Article
- Quantitative Structural Description of Zeolites by Machine Learning Analysis of Infrared Spectra.Inorganic chemistry · 2023Article
- Deducing subnanometer cluster size and shape distributions of heterogeneous supported catalysts.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
There is a need to characterize complex materials and their dynamics under reaction conditions to accelerate materials design. Adsorbate vibrational excitations are selective to adsorbate/surface interactions and infrared (IR) spectra associated with activating adsorbate vibrational modes are accurate, capture details of most modes, and can be obtained operando. Current interpretation depends on heuristic peak assignments for simple spectra, precluding the possibility of obtaining detailed structural information. Here, we combine data-based approaches with chemistry-dependent problem formulation to develop physics-driven surrogate models that generate synthetic IR spectra from first-principles calculations. Using synthetic IR spectra of carbon monoxide on platinum, we implement multinomial regression via neural network ensembles to learn probability distributions functions (pdfs) that describe adsorption sites and quantify uncertainty. We use these pdfs to infer detailed surface microstructure from experimental spectra and extend this methodology to other systems as a first step towards characterizing complex interfaces and closing the materials gap.
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.