About: npj Computational Materials

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Academic journal published by Nature Publishing Group , covering the subjects: Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials | Science: Mathematics:

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description	academisch tijdschrift (nl) Academic journal published by Nature Publishing Group , covering the subjects: Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials \| Science: Mathematics: (en)
language of work or name	wds:Q50815324-0FCE1EEF-4E35-48E0-9BE0-11347014545B
language of work or name	English
Scilit journal ID	wds:Q50815324-98288293-113F-41B3-BF91-9278BBD2E792
Crossref journal ID	wds:Q50815324-7882AF0A-E7EE-41D9-ADF2-AF766657C74D
Scilit journal ID	1357024
Crossref journal ID	296514
rdfs:label	npj Computational Materials (en) npj Computational Materials (nl)
skos:prefLabel	npj Computational Materials (en) npj Computational Materials (nl)
name	npj Computational Materials (en) npj Computational Materials (nl)
title	wds:Q50815324-2D036C9A-75A5-4F25-83A4-6C6740E53A90
title	npj computational materials (en)
Directory of Open Access Journals ID	wds:Q50815324-BBB9C270-6431-419F-8CBB-85EA152736DA
Directory of Open Access Journals ID	2057-3960
ACNP journal ID	wds:Q50815324-2AF7590E-1475-4CA7-B9B4-59700EA08806
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HAL journal ID	129708
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catalog	wds:Q50815324-A3C8CD35-39AE-4496-B5FC-C891893B5BC1
instance of	scientific journal open-access journal
catalog	Directory of Open Access Journals
award received	wds:Q50815324-92D58BC0-1AD2-4E4E-A28E-5A7426135F6A
award received	DOAJ seal
country	wds:Q50815324-510514E1-1907-443D-93FF-FDADFFD78F5A
country	United Kingdom
main subject	wds:Q50815324-B2564600-0FD5-4EC6-8862-1ED6070C560E
main subject	materials science
publisher	wds:Q50815324-8A743771-6DB6-470E-8BD8-0D7BBB8E1351
publisher	Nature Portfolio
bibcode	wds:Q50815324-16c5f7f6-4635-175f-6ea0-884a9942896b
bibcode	npjCM
OpenAlex ID	wds:Q50815324-76DF59E6-56E5-4D46-B46A-A11DDA0BD40E
copyright license	wds:Q50815324-221AF2F1-2B4C-45A1-AE66-25A5A2C09874
country of origin	wds:Q50815324-25370AFF-BD59-4C6F-B5BC-A9775FA974F2
copyright status	wds:Q50815324-223709E1-8AE5-4509-85BD-FB30062D16A8
official website	wds:Q50815324-3B52709E-2C0B-4AE0-A6E0-0523B6E1A4DE wds:Q50815324-AF32D4A7-D064-40F7-BC70-AE9F62F4BD45 wds:Q50815324-C1142FD2-8F82-493F-8690-3BA057B20303
OpenAlex ID	V4210232664
copyright license	Creative Commons Attribution
country of origin	United Kingdom
copyright status	copyrighted
official website	http://search.proquest.com/publication/2041924 http://www.nature.com/npjcompumats/ http://bibpurl.oclc.org/web/80437
Scopus source ID	wds:Q50815324-0466FA25-CAE9-483D-A8E2-CEBC00796ECE
ISSN	wds:Q50815324-E5BAAB1F-7A65-4321-8200-A57E1C18AE0D
ISSN-L	wds:Q50815324-5398F848-2FCF-43C7-A39F-E4171DB3CB72
indexed in bibliographic review	wds:Q50815324-9638975C-617C-467F-AA3E-A19374070860
ISSN	urn:ISSN:2057-3960
ISSN-L	http://issn.org/resource/ISSN/2057-3960
Scopus source ID	21100850798
ISSN	2057-3960
ISSN-L	2057-3960
indexed in bibliographic review	Scopus
Dimensions Source ID	wds:Q50815324-B834D96E-D7F3-4B0C-A012-D7DC82DCD371
Paperity journal ID	wds:Q50815324-7E4CA552-F699-4F9F-BA2F-45AAD02F94CF
Dimensions Source ID	1285194
Paperity journal ID	123098
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q50815324
is relative to of	wds:Q30297410-23B4B162-03F2-4968-822C-8CFC9EDBFF53
is published in of	Effective mass and Fermi surface complexity factor from ab initio band structure calculations A general-purpose machine learning framework for predicting properties of inorganic materials Phonon broadening in high entropy alloys Temperature-dependent phonon spectra of magnetic random solid solutions Ab initio electron-defect interactions using Wannier functions Beyond the RPA and GW methods with adiabatic xc-kernels for accurate ground state and quasiparticle energies Electron transfer governed by light–matter interaction at metal–semiconductor interface Unfolding the complexity of phonon quasi-particle physics in disordered materials The ReaxFF reactive force-field: development, applications and future directions Virtual screening of inorganic materials synthesis parameters with deep learning Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design Coarse-graining auto-encoders for molecular dynamics Automated defect analysis in electron microscopic images Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics Reliable and explainable machine-learning methods for accelerated material discovery Magnetic and superconducting phase diagrams and transition temperatures predicted using text mining and machine learning Highly selective phonon diffusive scattering in superionic layered AgCrSe2 Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures Electrochemically driven conversion reaction in fluoride electrodes for energy storage devices Topology-optimized thermal metamaterials traversing full-parameter anisotropic space High-throughput computational discovery of In2Mn2O7 as a high Curie temperature ferromagnetic semiconductor for spintronics Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods A machine learning approach to model solute grain boundary segregation Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning Empirical modeling of dopability in diamond-like semiconductors Machine-learned multi-system surrogate models for materials prediction Tunable ferromagnetic Weyl fermions from a hybrid nodal ring Ultralow lattice thermal conductivity of chalcogenide perovskite CaZrSe3 contributes to high thermoelectric figure of merit A property-oriented design strategy for high performance copper alloys via machine learning Strong phonon localization in PbTe with dislocations and large deviation to Matthiessen’s rule

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