A spatial cluster method for prime farmland selection

2007 ◽  
Author(s):  
Xinqi Zheng ◽  
Weining Xiang ◽  
Jinwei Dong ◽  
Buqing Zhong
2004 ◽  
Vol 14 (4) ◽  
pp. 337-342 ◽  
Author(s):  
De-min Zhou ◽  
Jian-chun Xu ◽  
John Radke ◽  
Lan Mu

1998 ◽  
Vol 94 (1) ◽  
pp. 181-187 ◽  
Author(s):  
EPHRAIM ELIAV ◽  
UZI KALDOR ◽  
YASUYUKI ISHIKAWA

2018 ◽  
Vol 5 (86) ◽  
pp. 25-35
Author(s):  
G.G. Rapakov ◽  
E.A. Lebedeva ◽  
V.A. Gorbunov ◽  
K.A. Abdalov ◽  
O.V. Mel'nichuk

2020 ◽  
Author(s):  
Soumi Haldar ◽  
Achintya Kumar Dutta

We have presented a multi-layer implementation of the equation of motion coupled-cluster method for the electron affinity, based on local and pair natural orbitals. The method gives consistent accuracy for both localized and delocalized anionic states. It results in many fold speedup in computational timing as compared to the canonical and DLPNO based implementation of the EA-EOM-CCSD method. We have also developed an explicit fragment-based approach which can lead to even higher speed-up with little loss in accuracy. The multi-layer method can be used to treat the environmental effect of both bonded and non-bonded nature on the electron attachment process in large molecules.<br>


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Alexander G. Donchev ◽  
Andrew G. Taube ◽  
Elizabeth Decolvenaere ◽  
Cory Hargus ◽  
Robert T. McGibbon ◽  
...  

AbstractAdvances in computational chemistry create an ongoing need for larger and higher-quality datasets that characterize noncovalent molecular interactions. We present three benchmark collections of quantum mechanical data, covering approximately 3,700 distinct types of interacting molecule pairs. The first collection, which we refer to as DES370K, contains interaction energies for more than 370,000 dimer geometries. These were computed using the coupled-cluster method with single, double, and perturbative triple excitations [CCSD(T)], which is widely regarded as the gold-standard method in electronic structure theory. Our second benchmark collection, a core representative subset of DES370K called DES15K, is intended for more computationally demanding applications of the data. Finally, DES5M, our third collection, comprises interaction energies for nearly 5,000,000 dimer geometries; these were calculated using SNS-MP2, a machine learning approach that provides results with accuracy comparable to that of our coupled-cluster training data. These datasets may prove useful in the development of density functionals, empirically corrected wavefunction-based approaches, semi-empirical methods, force fields, and models trained using machine learning methods.


2021 ◽  
Vol 62 (4) ◽  
Author(s):  
Khaled Younes ◽  
Bradley Gibeau ◽  
Sina Ghaemi ◽  
Jean-Pierre Hickey

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