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Title: Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles

Journal Article · · Journal of Physical Chemistry Letters
; ; ;

Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Organization:
~OTHER
DOE Contract Number:
SC0012704
OSTI ID:
1808984
Report Number(s):
BNL-221879-2021-JACI
Journal Information:
Journal of Physical Chemistry Letters, Vol. 8, Issue 20
Country of Publication:
United States
Language:
English

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Data‐Driven Materials Science: Status, Challenges, and Perspectives journal September 2019
A Critical Review of Machine Learning of Energy Materials journal January 2020
Machine learning for heterogeneous catalyst design and discovery journal May 2018
Formation and Functioning of Bimetallic Nanocatalysts: The Power of X-ray Probes journal July 2019
The Rise of Catalyst Informatics: Towards Catalyst Genomics journal January 2019
Synchrotron Big Data Science journal September 2018
Nanomaterials Discovery and Design through Machine Learning journal January 2019
Local Structure Study of Lanthanide Elements by X‐Ray Absorption Near Edge Structure Spectroscopy journal September 2018
Absorption of Hydrocarbons on Palladium Catalysts: From Simple Models Towards Machine Learning Analysis of X-ray Absorption Spectroscopy Data journal January 2020
CO2 hydrogenation to high-value products via heterogeneous catalysis journal December 2019
Spatial and temporal exploration of heterogeneous catalysts with synchrotron radiation journal August 2018
Towards calibration-invariant spectroscopy using deep learning journal February 2019
Machine learning in catalysis journal April 2018
High-throughput computational X-ray absorption spectroscopy journal July 2018
The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics journal January 2018
Collaboration between experiment and theory in solar fuels research journal January 2019
Perspectives on the design of nanoparticle systems for catalysis journal January 2018
Exploratory machine-learned theoretical chemical shifts can closely predict metabolic mixture signals journal January 2018
Understanding supported noble metal catalysts using first-principles calculations journal November 2019
Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning journal October 2019
Quantitative estimation of properties from core-loss spectrum via neural network journal March 2019
Convolutional neural networks for grazing incidence x-ray scattering patterns: thin film structure identification journal March 2019
Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra text January 2019
Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts text January 2018
Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra journal January 2019
Formation and Functioning of Bimetallic Nanocatalysts: The Power of X‐ray Probes journal July 2019
The TensorMol-0.1 Model Chemistry: a Neural Network Augmented with Long-Range Physics preprint January 2017
Data-driven materials science: status, challenges and perspectives text January 2019
Tuning the Structure of Pt Nanoparticles through Support Interactions: An in Situ Polarized X-ray Absorption Study Coupled with Atomistic Simulations journal March 2019
Operando X-ray Absorption Spectroscopy Investigation of Photocatalytic Hydrogen Evolution over Ultradispersed Pt/TiO 2 Catalysts journal October 2020
Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials journal March 2020
Data-driven approach for the prediction and interpretation of core-electron loss spectroscopy journal September 2018
Melting properties by X-ray absorption spectroscopy: common signatures in binary Fe–C, Fe–O, Fe–S and Fe–Si systems journal July 2020

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