Construction of diabatic energy surfaces for LiFH with artificial neural networks.

about

Construction of diabatic energy surfaces for LiFH with artificial neural networks.

Item

http://www.wikidata.org/entity/Q47301817

description

2017 nî lūn-bûn

@nan

2017年の論文

@ja

2017年学术文章

@wuu

2017年学术文章

@zh

2017年学术文章

@zh-cn

2017年学术文章

@zh-hans

2017年学术文章

@zh-my

2017年学术文章

@zh-sg

2017年學術文章

@yue

2017年學術文章

@zh-hant

name

Construction of diabatic energy surfaces for LiFH with artificial neural networks.

@en

Construction of diabatic energy surfaces for LiFH with artificial neural networks.

@nl

type

Item

label

Construction of diabatic energy surfaces for LiFH with artificial neural networks.

@en

Construction of diabatic energy surfaces for LiFH with artificial neural networks.

@nl

prefLabel

Construction of diabatic energy surfaces for LiFH with artificial neural networks.

@en

Construction of diabatic energy surfaces for LiFH with artificial neural networks.

@nl

P1476

Construction of diabatic energy surfaces for LiFH with artificial neural networks.

@en

P2093

Bina Fu

http://www.w3.org/2001/XMLSchema#string

Dong H Zhang

http://www.w3.org/2001/XMLSchema#string

Yafu Guan

http://www.w3.org/2001/XMLSchema#string

P2860

Diabatic and Adiabatic Representations for Atomic Collision Problems

Toward eliminating the electronic structure bottleneck in nonadiabatic dynamics on the fly: an algorithm to fit nonlocal, quasidiabatic, coupled electronic state Hamiltonians based on ab initio electronic structure data.

Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations.

First-principles quantum dynamical theory for the dissociative chemisorption of H2O on rigid Cu(111).

Potential energy surfaces fitted by artificial neural networks.

Neural network based coupled diabatic potential energy surfaces for reactive scattering.

Artificial neural networks for solving ordinary and partial differential equations.

Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks.

Beyond Born-Oppenheimer theory for ab initio constructed diabatic potential energy surfaces of singlet H3+ to study reaction dynamics using coupled 3D time-dependent wave-packet approach.

Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials.

P304

224307

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.1063/1.5007031

http://www.w3.org/2001/XMLSchema#string

P407

English

P433

http://www.w3.org/2001/XMLSchema#string

P478

147

http://www.w3.org/2001/XMLSchema#string

P577

2017-12-01T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P698

29246076

http://www.w3.org/2001/XMLSchema#string