Add to ORKGIn this part we try to study different DFT functionals and comparing with some reference data for some selected molecules. For that purpose, we are describing DFT, and different forms that exist of this theory and everything needed for the calculation. Then, the hardest task of this part was to build scripts that allow to automatize the whole process, and building a clever and relational database to store all relevant information. Finally, some calculations were performed using those scripts and where we compare several DFT functionals with published information.Departamento de Química Física y Química InorgánicaSCM Company, the NetherlandsMáster en Química Teórica y Modelización Computaciona
Neural networks and other machine learning approaches have been successfully used to accurately repr...
Molecular mechanics is the tool of choice for the modeling of systems that are so large or complex t...
Artificial neural networks are fitted to molecular dynamics trajectories using the Behler-Parrinello...
Development and applications of neural network (NN)-based approaches for representing potential ener...
We demonstrate how a deep neural network (NN) trained on a data set of quantum mechanical (QM) DFT c...
Die Simulation realistischer Festkörperoberflächen, wie zum Beispiel die aktive Oberfläche eines het...
We implement a method for constructing analytic interatomic potentials by fitting artificial neural ...
It is shown that neural networks (NNs) are efficient and effective tools for fitting potential energ...
A more flexible neural network (NN) method using the fundamental invariants (FIs) as the input vecto...
The discovery of molecules with specific properties is crucial to developing effective materials and...
datasets used for training and evaluating neural networks in the following work: Neural Network Pote...
peer reviewedWe combine density-functional tight binding (DFTB) with deep tensor neural networks (DT...
Texto completo: acesso restrito. p. 281–288The fitting of ab initio electronic energies of polyatomi...
Density Functional Theorem (DFT)-based methods play a major role in modern computational chemistry. ...
Molecular mechanics is the tool of choice for the modeling of systems that are so large or complex t...
Neural networks and other machine learning approaches have been successfully used to accurately repr...
Molecular mechanics is the tool of choice for the modeling of systems that are so large or complex t...
Artificial neural networks are fitted to molecular dynamics trajectories using the Behler-Parrinello...
Development and applications of neural network (NN)-based approaches for representing potential ener...
We demonstrate how a deep neural network (NN) trained on a data set of quantum mechanical (QM) DFT c...
Die Simulation realistischer Festkörperoberflächen, wie zum Beispiel die aktive Oberfläche eines het...
We implement a method for constructing analytic interatomic potentials by fitting artificial neural ...
It is shown that neural networks (NNs) are efficient and effective tools for fitting potential energ...
A more flexible neural network (NN) method using the fundamental invariants (FIs) as the input vecto...
The discovery of molecules with specific properties is crucial to developing effective materials and...
datasets used for training and evaluating neural networks in the following work: Neural Network Pote...
peer reviewedWe combine density-functional tight binding (DFTB) with deep tensor neural networks (DT...
Texto completo: acesso restrito. p. 281–288The fitting of ab initio electronic energies of polyatomi...
Density Functional Theorem (DFT)-based methods play a major role in modern computational chemistry. ...
Molecular mechanics is the tool of choice for the modeling of systems that are so large or complex t...
Neural networks and other machine learning approaches have been successfully used to accurately repr...
Molecular mechanics is the tool of choice for the modeling of systems that are so large or complex t...
Artificial neural networks are fitted to molecular dynamics trajectories using the Behler-Parrinello...