Classification of local chemical environments from x-ray absorption spectra using supervised machine learning
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Classification of local chemical environments from x-ray absorption spectra using supervised machine learning
Authors
Keywords
-
Journal
PHYSICAL REVIEW MATERIALS
Volume 3, Issue 3, Pages -
Publisher
American Physical Society (APS)
Online
2019-03-13
DOI
10.1103/physrevmaterials.3.033604
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Neural Network Approach for Characterizing Structural Transformations by X-Ray Absorption Fine Structure Spectroscopy
- (2018) Janis Timoshenko et al. PHYSICAL REVIEW LETTERS
- Accelerating the discovery of materials for clean energy in the era of smart automation
- (2018) Daniel P. Tabor et al. Nature Reviews Materials
- Automated generation and ensemble-learned matching of X-ray absorption spectra
- (2018) Chen Zheng et al. npj Computational Materials
- Electron-Hole Theory of the Effect of Quantum Nuclei on the X-Ray Absorption Spectra of Liquid Water
- (2018) Zhaoru Sun et al. PHYSICAL REVIEW LETTERS
- High-throughput computational X-ray absorption spectroscopy
- (2018) Kiran Mathew et al. Scientific Data
- Spatial and temporal exploration of heterogeneous catalysts with synchrotron radiation
- (2018) Florian Meirer et al. Nature Reviews Materials
- Probing Atomic Distributions in Mono- and Bimetallic Nanoparticles by Supervised Machine Learning
- (2018) Janis Timoshenko et al. NANO LETTERS
- Identification of dopant site and its effect on electrochemical activity in Mn-doped lithium titanate
- (2018) Harishchandra Singh et al. PHYSICAL REVIEW MATERIALS
- Machine learning in materials design and discovery: Examples from the present and suggestions for the future
- (2018) J. E. Gubernatis et al. PHYSICAL REVIEW MATERIALS
- Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles
- (2017) Janis Timoshenko et al. Journal of Physical Chemistry Letters
- Multi-Stage Structural Transformations in Zero-Strain Lithium Titanate Unveiled by in Situ X-ray Absorption Fingerprints
- (2017) Wei Zhang et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
- Learning phase transitions by confusion
- (2017) Evert P. L. van Nieuwenburg et al. Nature Physics
- Accurate X-Ray Spectral Predictions: An Advanced Self-Consistent-Field Approach Inspired by Many-Body Perturbation Theory
- (2017) Yufeng Liang et al. PHYSICAL REVIEW LETTERS
- Solving the quantum many-body problem with artificial neural networks
- (2017) Giuseppe Carleo et al. SCIENCE
- ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
- (2017) J. S. Smith et al. Chemical Science
- Machine learning of accurate energy-conserving molecular force fields
- (2017) Stefan Chmiela et al. Science Advances
- Solving local structure around dopants in metal nanoparticles with ab initio modeling of X-ray absorption near edge structure
- (2016) Janis Timoshenko et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- The Materials Application Programming Interface (API): A simple, flexible and efficient API for materials data based on REpresentational State Transfer (REST) principles
- (2015) Shyue Ping Ong et al. COMPUTATIONAL MATERIALS SCIENCE
- Design of Semiconducting TetrahedralMn1−xZnxOAlloys and Their Application to Solar Water Splitting
- (2015) Haowei Peng et al. Physical Review X
- Effects of Adsorbate Coverage and Bond-Length Disorder on the d-Band Center of Carbon-Supported Pt Catalysts
- (2014) Matthew W. Small et al. CHEMPHYSCHEM
- exciting: a full-potential all-electron package implementing density-functional theory and many-body perturbation theory
- (2014) Andris Gulans et al. JOURNAL OF PHYSICS-CONDENSED MATTER
- Origins of extreme broadening mechanisms in near-edge x-ray spectra of nitrogen compounds
- (2014) John Vinson et al. PHYSICAL REVIEW B
- EXAFS and XANES analysis of oxides at the nanoscale
- (2014) Alexei Kuzmin et al. IUCrJ
- Reactivity of Surface Species in Heterogeneous Catalysts Probed by In Situ X-ray Absorption Techniques
- (2013) Silvia Bordiga et al. CHEMICAL REVIEWS
- Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
- (2013) Anubhav Jain et al. APL Materials
- Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
- (2012) Shyue Ping Ong et al. COMPUTATIONAL MATERIALS SCIENCE
- Defects in room-temperature ferromagnetic Cu-doped ZnO films probed by x-ray absorption spectroscopy
- (2012) Q Ma et al. JOURNAL OF PHYSICS-CONDENSED MATTER
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- (2012) Matthias Rupp et al. PHYSICAL REVIEW LETTERS
- Synchrotron Techniques for In Situ Catalytic Studies: Capabilities, Challenges, and Opportunities
- (2012) Anatoly I. Frenkel et al. ACS Catalysis
- Bethe-Salpeter equation calculations of core excitation spectra
- (2011) J. Vinson et al. PHYSICAL REVIEW B
- Evidence of Cobalt-Vacancy Complexes inZn1−xCoxODilute Magnetic Semiconductors
- (2011) G. Ciatto et al. PHYSICAL REVIEW LETTERS
- Parameter-free calculations of X-ray spectra with FEFF9
- (2010) John J. Rehr et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- X-Ray Absorption Signatures of the Molecular Environment in Water and Ice
- (2010) Wei Chen et al. PHYSICAL REVIEW LETTERS
- Battery materials for ultrafast charging and discharging
- (2009) Byoungwoo Kang et al. NATURE
- Intrinsic charge transfer gap in NiO fromNi K-edge x-ray absorption spectroscopy
- (2009) C. Gougoussis et al. PHYSICAL REVIEW B
- Ab initio theory and calculations of X-ray spectra
- (2008) John J. Rehr et al. COMPTES RENDUS PHYSIQUE
- Assignment of pre-edge peaks in K-edge x-ray absorption spectra of 3d transition metal compounds: electric dipole or quadrupole?
- (2008) Takashi Yamamoto X-RAY SPECTROMETRY
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreDiscover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversation