Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture.
about
In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithmMaterials Genome in Action: Identifying the Performance Limits of Physical Hydrogen StorageMetal-organic framework materials for light-harvesting and energy transfer.Impact of distributions on the archetypes and prototypes in heterogeneous nanoparticle ensembles.Cutting the cost of carbon capture: a case for carbon capture and utilization.Database for CO2 Separation Performances of MOFs Based on Computational Materials Screening.Genetic engineering of inorganic functional modular materials.Artificial neural network analysis of the catalytic efficiency of platinum nanoparticlesMachine learning meets volcano plots: computational discovery of cross-coupling catalystsCommittee machine that votes for similarity between materials
P2860
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P2860
Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture.
description
2014 nî lūn-bûn
@nan
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
2014年论文
@zh
2014年论文
@zh-cn
name
Rapid and Accurate Machine Lea ...... ic Frameworks for CO2 Capture.
@ast
Rapid and Accurate Machine Lea ...... ic Frameworks for CO2 Capture.
@en
type
label
Rapid and Accurate Machine Lea ...... ic Frameworks for CO2 Capture.
@ast
Rapid and Accurate Machine Lea ...... ic Frameworks for CO2 Capture.
@en
prefLabel
Rapid and Accurate Machine Lea ...... ic Frameworks for CO2 Capture.
@ast
Rapid and Accurate Machine Lea ...... ic Frameworks for CO2 Capture.
@en
P2093
P356
P1476
Rapid and Accurate Machine Lea ...... nic Frameworks for CO2 Capture
@en
P2093
Mohammad Zein Aghaji
Peter G Boyd
P304
P356
10.1021/JZ501331M
P577
2014-08-25T00:00:00Z