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RESEARCH PAPER · npj Computational Materials · 2020

Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships

Steven B. Torrisi, Matthew R. Carbone, Brian A. Rohr, Joseph H. Montoya, Yang Ha, Junko Yano, Santosh K. Suram, Linda Hung

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Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationshipsOriginalAccelerated discovery of stable spinels in energy systems via machine learning56Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses110Machine learning powered by principal component descriptors as the key for sorted structural fit of XANES16The rise of X-ray spectroscopies for unveiling the functional mechanisms in batteries37Unsupervised machine learning for unbiased chemical classification in X-ray absorption spectroscopy and X-ray emission spectroscopy52Latent Representation Learning for Structural Characterization of Catalysts50Progress in the Theory of X-ray Spectroscopy: From Quantum Chemistry to Machine Learning and Ultrafast Dynamics78Deciphering the Phillips Catalyst by Orbital Analysis and Supervised Machine Learning from Cr Pre-edge XANES of Molecular Libraries73Molecular excited states through a machine learning lens200Database of ab initio L-edge X-ray absorption near edge structure51Catalyze Materials Science with Machine Learning67Machine learning on neutron and x-ray scattering and spectroscopies116A machine learning approach to predict L-edge x-ray absorption spectra of light transition metal ion compounds3Comparative analysis of XANES and EXAFS for local structural characterization of disordered metal oxides35Unsupervised discovery of thin-film photovoltaic materials from unlabeled data37Full spectrum optical constant interface to the Materials Project0Insights into the adsorption of pharmaceuticals and personal care products (PPCPs) on biochar and activated carbon with the aid of machine learning212Machine learning exploration of the direct and indirect roles of Fe impregnation on Cr(VI) removal by engineered biochar100Search for Analytical Relations between X-Ray Absorption Spectra Descriptors and the Local Atomic Structure Using Machine Learning7Full spectrum optical constant interface to the Materials Project9Feature importance in multi-dimensional tissue-engineering datasets: Random forest assisted optimization of experimental variables for collagen scaffolds12A Genetic Algorithm-Based Artificial Network Method for Material Feature Recombination1Screening of potential double perovskite materials for photovoltaic applications using agglomerative hierarchical clustering1Targeted design of advanced electrocatalysts by machine learning110Speciation of Ru Molecular Complexes in a Homogeneous Catalytic System: Fingerprint XANES Analysis Guided by Machine Learning14Understanding X-ray absorption spectra by means of descriptors and machine learning algorithms186XAS Data Preprocessing of Nanocatalysts for Machine Learning Applications21Background0Beyond structural insight: a deep neural network for the prediction of Pt L 2/3 -edge X-ray absorption spectra12High-throughput oxygen chemical potential engineering of perovskite oxides for chemical looping applications110Machine learning for design principles for single atom catalysts towards electrochemical reactions116Machine Learning Spectral Indicators of Topology1New challenges in oxygen reduction catalysis: a consortium retrospective to inform future research53Plot2Spectra: an automatic spectra extraction tool8Toward autonomous materials research: Recent progress and future challenges61Direct Visualization of Structures of Atmospheric Corrosion Products on the Steel Surfaces by Two-dimensionally X-ray Absorption Fine Structure2Perspective on Theoretical Models for CO2 Electrochemical Reduction67Agents for sequential learning using multiple-fidelity data38Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network43Recent advances and applications of deep learning methods in materials science1161The case for data science in experimental chemistry: examples and recommendations83Extracting Structural Motifs from Pair Distribution Function Data of Nanostructures using Explainable Machine Learning2Materials Data toward Machine Learning: Advances and Challenges62Automated Bonding Analysis with Crystal Orbital Hamilton Populations64Green’s function methods for excited states and x-ray spectra of functional materials5Informed Chemical Classification of Organophosphorus Compounds via Unsupervised Machine Learning of X-ray Absorption Spectroscopy and X-ray Emission Spectroscopy22A Learning Assisted Approach for Electronic and Optical Characterization of Tin-Doped Titanium Oxide2Quantitatively Determining Surface–Adsorbate Properties from Vibrational Spectroscopy with Interpretable Machine Learning106When not to use machine learning: A perspective on potential and limitations51Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning41Machine‐Learning Spectral Indicators of 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optimization, predictions, and experimental insights for engineering high-performance MnO2 supercapacitors12Hamiltonian parameter inference from resonant inelastic x-ray scattering with active learning0A survey of AI-supported materials informatics7Interpretable Graph Neural Network for Predicting Transient Electronic Structures of Semiconductor Quantum Dots3Predicting Cycle Life for Lithium-Ion Batteries with Ternary Cathode Materials Using Data-Driven Machine Learning0

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