ArticlePatterns (New York, N.Y.)2020
Random Forest Models for Accurate Identification of Coordination Environments from X-Ray Absorption Near-Edge Structure.
Article in Patterns (New York, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Spectroscopy-informed XANES-PXRD framework for multi-property prediction and structure inference.Chemical science · 2026Article
- Unveiling sodium storage mechanisms in hard carbonChemical science · 2026Article
- A new framework for X-ray absorption spectroscopy data analysis based on machine learning: XASDAML.Journal of synchrotron radiation · 2025Article
- AABBA Graph Kernel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning.Journal of chemical information and modeling · 2024Article
- Real-time tracking of structural evolution in 2D MXenes using theory-enhanced machine learning.Scientific reports · 2024Article
- Improving sensitivity of XANES structural fit to the bridged metal-metal coordination.Journal of synchrotron radiation · 2024Article
- Deep Reinforcement Learning Environment Approach Based on Nanocatalyst XAS Diagnostics Graphic Formalization.Materials (Basel, Switzerland) · 2023Article
- Machine learning for a sustainable energy future.Nature reviews. Materials · 2023Review
- Artificial Intelligence Applied to Battery Research: Hype or Reality?Chemical reviews · 2022Review
- The case for data science in experimental chemistry: examples and recommendations.Nature reviews. Chemistry · 2022Review
- Review
- Database of ab initio L-edge X-ray absorption near edge structure.Scientific data · 2021Article
- Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks.Patterns (New York, N.Y.) · 2021Article
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Authors and funding
4 authors.
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Abstract
Analyzing coordination environments using X-ray absorption spectroscopy has broad applications in solid-state physics and material chemistry. Here, we show that random forest models trained on 190,000 K-edge X-ray absorption near-edge structure (XANES) spectra can identify the main atomic coordination environment with a high accuracy of 85.4% and all associated coordination environments with a high Jaccard score of 81.8% for 33 cation elements in oxides, significantly outperforming other machine-learning models. In a departure from prior works, the coordination environment is described as a distribution over 25 distinct coordination motifs with coordination numbers ranging from 1 to 12. More importantly, we show that the random forest models can be used to predict coordination environments from experimental K-edge XANES with minimal loss in accuracy. A drop-variable feature importance analysis highlights the key roles that the pre-edge and main-peak regions play in coordination environment identification.
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