about
Universal fragment descriptors for predicting properties of inorganic crystals.High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory.A hybrid organic-inorganic perovskite datasetComputer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure.Identifying an efficient, thermally robust inorganic phosphor host via machine learningFinding New Perovskite Halides via Machine LearningMachine learning assisted optimization of electrochemical properties for Ni-rich cathode materialsAccelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learningDeep neural networks for accurate predictions of crystal stability
P2860
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P2860
description
2016 nî lūn-bûn
@nan
2016年の論文
@ja
2016年学术文章
@wuu
2016年学术文章
@zh-cn
2016年学术文章
@zh-hans
2016年学术文章
@zh-my
2016年学术文章
@zh-sg
2016年學術文章
@yue
2016年學術文章
@zh
2016年學術文章
@zh-hant
name
Machine learning bandgaps of double perovskites.
@ast
Machine learning bandgaps of double perovskites.
@en
type
label
Machine learning bandgaps of double perovskites.
@ast
Machine learning bandgaps of double perovskites.
@en
prefLabel
Machine learning bandgaps of double perovskites.
@ast
Machine learning bandgaps of double perovskites.
@en
P2093
P2860
P356
P1433
P1476
Machine learning bandgaps of double perovskites.
@en
P2093
A Mannodi-Kanakkithodi
J E Gubernatis
R Ramprasad
P2860
P2888
P356
10.1038/SREP19375
P407
P577
2016-01-19T00:00:00Z