Pre-trained model for DTNN and CNN. related to 10.1002/advs.201801367 Pretrained DTNN model to predict energies Saved in cs-171 /l/ghoshk1/thesis/experiments/Annika_new_132k/deep_tensor_energies_mse_cost_optimized_multiple_RUN
This is a methodological guide to the use of deep neural networks in the processing of pulsed dipola...
Modern functional materials consist of large molecular building blocks with significant chemical com...
Trained Transformer model as described and used in the publication of " Molecular optimization by ca...
| openaire: EC/H2020/676580/EU//NoMaDDeep learning methods for the prediction of molecular excitatio...
We have updated and applied a convolutional neural network (CNN) machine-learning model to discover ...
Machine learning ML approaches have demonstrated the ability to predict molecular spectra at a fra...
Machine learning (ML) approaches have demonstrated the ability to predict molecular spectra at a fra...
Nuclear magnetic resonance spectroscopy (MRS) allows for the determination of atomic structures and ...
$^{1}$ D. E. Rumelhart and J. L. McClelland, Parallel Distributed Processing, Vol. 1, MIT Press, Cam...
Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biolo...
In this work we show that deep learning (DL) can be used for exploring complex and highly nonlinear ...
Various neural networks, including a single layer neural network (SLNN), a deep neural network (DNN)...
Many chemical and biological reactions, including ligand exchange processes, require thermal energy ...
State-of-the-art identification of the functional groups present in an unknown chemical entity requi...
A deep convolutional neural network (CNN) is developed to study symmetry energy (Esym(ρ)) effects by...
This is a methodological guide to the use of deep neural networks in the processing of pulsed dipola...
Modern functional materials consist of large molecular building blocks with significant chemical com...
Trained Transformer model as described and used in the publication of " Molecular optimization by ca...
| openaire: EC/H2020/676580/EU//NoMaDDeep learning methods for the prediction of molecular excitatio...
We have updated and applied a convolutional neural network (CNN) machine-learning model to discover ...
Machine learning ML approaches have demonstrated the ability to predict molecular spectra at a fra...
Machine learning (ML) approaches have demonstrated the ability to predict molecular spectra at a fra...
Nuclear magnetic resonance spectroscopy (MRS) allows for the determination of atomic structures and ...
$^{1}$ D. E. Rumelhart and J. L. McClelland, Parallel Distributed Processing, Vol. 1, MIT Press, Cam...
Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biolo...
In this work we show that deep learning (DL) can be used for exploring complex and highly nonlinear ...
Various neural networks, including a single layer neural network (SLNN), a deep neural network (DNN)...
Many chemical and biological reactions, including ligand exchange processes, require thermal energy ...
State-of-the-art identification of the functional groups present in an unknown chemical entity requi...
A deep convolutional neural network (CNN) is developed to study symmetry energy (Esym(ρ)) effects by...
This is a methodological guide to the use of deep neural networks in the processing of pulsed dipola...
Modern functional materials consist of large molecular building blocks with significant chemical com...
Trained Transformer model as described and used in the publication of " Molecular optimization by ca...
DOI