About: To address surface reaction network complexity using scaling relations machine learning and DFT calculations

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scientific article published on 06 March 2017

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rdf:type	Item
description	videnskabelig artikel (da) article científic (ca) articolo scientifico (it) artigo científico (pt) bilimsel makale (tr) vedecký článok (sk) vetenskaplig artikel (sv) vědecký článek (cs) wetenschappelijk artikel (nl) wissenschaftlicher Artikel (de) научни чланак (sr) article scientifique (fr) artículu científicu espublizáu en 2017 (ast) наукова стаття, опублікована в березні 2017 (uk) scientific article published on 06 March 2017 (en) مقالة علمية نشرت في 06 مارس 2017 (ar)
publication date	wds:Q37690206-B562438E-2800-45F6-B1E3-FE0EFD0D553B
publication date	2017-03-06 00:00:00Z (xsd:dateTime)
language of work or name	wds:Q37690206-40E1BE43-3886-4758-8F95-80EF76C575CB
language of work or name	English
exact match	wds:Q37690206-310297A8-3709-4096-9301-F65C7C916CAE
exact match	https://scigraph.springernature.com/pub.10.1038/ncomms14621
cites work	wds:Q37690206-05CF11A8-4B05-4DD9-8AFB-D79FB1169D03 wds:Q37690206-0CF9693E-F6D0-40C3-BB34-8952328659FB wds:Q37690206-52B619BB-CAF1-4C50-8078-50A354871863 wds:Q37690206-7BDB1987-AE80-4BB7-ADFF-70418A656D08 wds:Q37690206-9EC96C28-7FAC-4F0E-8036-B5723BF6FC0A wds:Q37690206-A4108CF7-3619-4AD2-BE7C-1CFA5BB53245 wds:Q37690206-AC96E140-25C6-4250-9C62-7F8A08A890D4 wds:Q37690206-B4E65533-BF13-4024-98D8-DF136B488B88 wds:Q37690206-E6F5E005-6B9A-4843-9405-38F13AB91B4A wds:Q37690206-ED46CA9B-F4F2-445F-BFFA-38066239FE4E
cites work	An algorithm for the reduction of genome-scale metabolic network models to meaningful core models. Special points for Brillouin-zone integrations Universally sloppy parameter sensitivities in systems biology models QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials Towards the computational design of solid catalysts. Intrinsic Selectivity and Structure Sensitivity of Rhodium Catalysts for C(2+) Oxygenate Production. Effects of correlated parameters and uncertainty in electronic-structure-based chemical kinetic modelling Assessing the reliability of calculated catalytic ammonia synthesis rates Density functionals for surface science: Exchange-correlation model development with Bayesian error estimation Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms
author name string	wds:Q37690206-C16FCC8A-8C22-437C-BB5F-EA851300EC96
author name string	Zachary W Ulissi
rdfs:label	To address surface reaction network complexity using scaling relations machine learning and DFT calculations (en) To address surface reaction network complexity using scaling relations machine learning and DFT calculations (nl) To address surface reaction network complexity using scaling relations machine learning and DFT calculations (ast)
skos:prefLabel	To address surface reaction network complexity using scaling relations machine learning and DFT calculations (en) To address surface reaction network complexity using scaling relations machine learning and DFT calculations (nl) To address surface reaction network complexity using scaling relations machine learning and DFT calculations (ast)
name	To address surface reaction network complexity using scaling relations machine learning and DFT calculations (en) To address surface reaction network complexity using scaling relations machine learning and DFT calculations (nl) To address surface reaction network complexity using scaling relations machine learning and DFT calculations (ast)
author	wds:Q37690206-A1E92FE9-8F41-47BD-A60F-EA6ECBC36A64 wds:Q37690206-DE68A200-4421-404C-980A-4D0ECE38F0E9 wds:Q37690206-E514189D-3D2C-4E97-BF6F-B52D924DAFC2
author	Jens Kehlet Nørskov Thomas Bligaard Andrew J Medford
title	wds:Q37690206-2B524179-79D1-4DA4-BED2-36316688920C
title	To address surface reaction network complexity using scaling relations machine learning and DFT calculations (en)
page(s)	wds:Q37690206-2F8C4DC1-CB23-4A6E-8FC8-3E51CB06FF0A
page(s)	14621
instance of	wds:Q37690206-D47DF6A0-4626-48A9-97BE-88AACA79A95A
instance of	scholarly article
main subject	wds:Q37690206-33735AB6-B1C3-486E-AA14-67E94F47C8AF
main subject	machine learning
PubMed ID	wds:Q37690206-8F4EBB05-3ECB-473F-8E7F-EEFD18B7B1EA
PubMed ID	http://rdf.ncbi.nlm.nih.gov/pubchem/reference/28262694
PubMed ID	28262694
published in	wds:Q37690206-6789324D-874E-49AD-AEE7-FEE9686B0311
published in	Nature Communications
issue	wds:Q37690206-0AAA6A5A-1AD7-41DD-8B12-B9C2DBE9E3AE
volume	wds:Q37690206-04B8E49B-87AE-4236-8BEB-756542B6481C
issue	1
volume	8
DOI	wds:Q37690206-37E6D634-1F3D-470E-8E04-E546C23723DC
DOI	http://dx.doi.org/10.1038/NCOMMS14621
DOI	10.1038/NCOMMS14621
copyright license	wds:Q37690206-67986483-eafb-46c5-910b-5f869fc0ffa7
copyright status	wds:Q37690206-bdff5cc3-8f53-45c3-926b-34c54162c276
copyright license	Creative Commons Attribution 4.0 International
copyright status	copyrighted
ADS bibcode	wds:Q37690206-359A3860-7023-4F1A-8D69-A13819CCD9B3
ADS bibcode	2017NatCo...814621U
PMCID	wds:Q37690206-1BA0B7A5-D005-4784-B395-18905C9DFEFA
PMCID	5343483
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q37690206
is cites work of	Addressing global uncertainty and sensitivity in first-principles based microkinetic models by an adaptive sparse grid approach. Perovskites in catalysis and electrocatalysis. Machine learning for quantum dynamics: deep learning of excitation energy transfer properties. wd:Q108527464 Optimization of the facet structure of transition-metal catalysts applied to the oxygen reduction reaction Machine learning meets volcano plots: computational discovery of cross-coupling catalysts Synthesis of methanol from CO2 hydrogenation promoted by dissociative adsorption of hydrogen on a Ga3Ni5(221) surface. Accurate force field for molybdenum by machine learning large materials data Active learning with non-ab initio input features toward efficient CO2 reduction catalysts† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc03422a. A quantitative uncertainty metric controls error in neural network-driven chemical discovery Dynamic vs static behaviour of a supported nanoparticle with reaction-induced catalytic sites in a lattice model

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