research
∙
12/11/2022
SchNetPack 2.0: A neural network toolbox for atomistic machine learning
SchNetPack is a versatile neural networks toolbox that addresses both th...
share
research
∙
03/30/2022
Automatic Identification of Chemical Moieties
In recent years, the prediction of quantum mechanical observables with m...
research
∙
09/10/2021
Inverse design of 3d molecular structures with conditional generative neural networks
The rational design of molecules with desired properties is a long-stand...
research
∙
05/01/2021
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
In recent years, machine-learned force fields (ML-FFs) have gained incre...
research
∙
02/05/2021
Equivariant message passing for the prediction of tensorial properties and molecular spectra
Message passing neural networks have become a method of choice for learn...
research
∙
10/28/2020
Machine learning of solvent effects on molecular spectra and reactions
Fast and accurate simulation of complex chemical systems in environments...
research
∙
10/14/2020
Machine Learning Force Fields
In recent years, the use of Machine Learning (ML) in computational chemi...
research
∙
06/05/2020
XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks
Graph Neural Networks (GNNs) are a popular approach for predicting graph...
research
∙
02/27/2020
Autonomous robotic nanofabrication with reinforcement learning
The ability to handle single molecules as effectively as macroscopic bui...
research
∙
06/02/2019
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Deep learning has proven to yield fast and accurate predictions of quant...
research
∙
12/11/2018
Learning representations of molecules and materials with atomistic neural networks
Deep Learning has been shown to learn efficient representations for stru...
research
∙
10/26/2018
Generating equilibrium molecules with deep neural networks
Discovery of atomistic systems with desirable properties is a major chal...
research
∙
10/23/2018
Analysis of Atomistic Representations Using Weighted Skip-Connections
In this work, we extend the SchNet architecture by using weighted skip c...
research
∙
08/13/2018
iNNvestigate neural networks!
In recent years, deep neural networks have revolutionized many applicati...
research
∙
06/27/2018
Quantum-chemical insights from interpretable atomistic neural networks
With the rise of deep neural networks for quantum chemistry applications...
research
∙
11/02/2017
The (Un)reliability of saliency methods
Saliency methods aim to explain the predictions of deep neural networks....
research
∙
06/26/2017
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Deep learning has the potential to revolutionize quantum chemistry as it...
research
∙
05/16/2017