Electronic spectra from TDDFT and machine learning in chemical space.
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Exploring experimental fitness landscapes for chemical synthesis and property optimization.Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels.The role of tachysterol in vitamin D photosynthesis - a non-adiabatic molecular dynamics study.MoleculeNet: a benchmark for molecular machine learning.
P2860
Electronic spectra from TDDFT and machine learning in chemical space.
description
2015 nî lūn-bûn
@nan
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
2015年论文
@zh
2015年论文
@zh-cn
name
Electronic spectra from TDDFT and machine learning in chemical space.
@en
type
label
Electronic spectra from TDDFT and machine learning in chemical space.
@en
prefLabel
Electronic spectra from TDDFT and machine learning in chemical space.
@en
P2093
P2860
P356
P1476
Electronic spectra from TDDFT and machine learning in chemical space.
@en
P2093
Enrico Tapavicza
Mia Hartmann
Raghunathan Ramakrishnan
P2860
P304
P356
10.1063/1.4928757
P407
P577
2015-08-01T00:00:00Z
P5875
P698
P818
1504.01966