Machine learning unifies the modeling of materials and molecules.
about
A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property data.Machine learning for the structure-energy-property landscapes of molecular crystals.Applying machine learning techniques to predict the properties of energetic materials.Machine learning meets volcano plots: computational discovery of cross-coupling catalystsChemical shifts in molecular solids by machine learningTowards exact molecular dynamics simulations with machine-learned force fields
P2860
Machine learning unifies the modeling of materials and molecules.
description
2017 nî lūn-bûn
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2017年の論文
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2017年学术文章
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2017年学术文章
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2017年学术文章
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2017年学术文章
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2017年学术文章
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2017年学术文章
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2017年學術文章
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2017年學術文章
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name
Machine learning unifies the modeling of materials and molecules.
@en
Machine learning unifies the modeling of materials and molecules.
@nl
type
label
Machine learning unifies the modeling of materials and molecules.
@en
Machine learning unifies the modeling of materials and molecules.
@nl
prefLabel
Machine learning unifies the modeling of materials and molecules.
@en
Machine learning unifies the modeling of materials and molecules.
@nl
P2860
P50
P356
P1433
P1476
Machine learning unifies the modeling of materials and molecules
@en
P2093
Carl Poelking
Noam Bernstein
P2860
P304
P356
10.1126/SCIADV.1701816
P577
2017-12-13T00:00:00Z
P698
P818
1706.00179